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Online learning.

• Lisa Marie Blaschke Lisa Marie Blaschke Carl von Ossietzky University
•  and  Svenja Bedenlier Svenja Bedenlier Friedrich-Alexander-University Erlangen-Nürnberg
• https://doi.org/10.1093/acrefore/9780190264093.013.674
• Published online: 30 April 2020

With the ubiquity of the Internet and the pedagogical opportunities that digital media afford for education on all levels, online learning constitutes a form of education that accommodates learners’ individual needs beyond traditional face-to-face instruction, allowing it to occur with the student physically separated from the instructor. Online learning and distance education have entered into the mainstream of educational provision at of most of the 21st century’s higher education institutions.

With its consequent focus on the learner and elements of course accessibility and flexibility and learner collaboration, online learning renegotiates the meaning of teaching and learning, positioning students at the heart of the process and requiring new competencies for successful online learners as well as instructors. New teaching and learning strategies, support structures, and services are being developed and implemented and often require system-wide changes within higher education institutions.

Drawing on central elements from the field of distance education, both in practice and in its theoretical foundations, online learning makes use of new affordances of a variety of information and communication technologies—ranging from multimedia learning objects to social and collaborative media and entire virtual learning environments. Fundamental learning theories are being revisited and discussed in the context of online learning, leaving room for their further development and application in the digital age.

• online learning
• online distance education
• digital media
• learning theory

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• Open access
• Published: 18 November 2023

Students’ online learning adaptability and their continuous usage intention across different disciplines

• Zheng Li 1 ,
• Xiaodong Lou 2 ,
• Minwei Chen 3 ,
• Siyu Li 1 ,
• Cixian Lv 4 ,
• Shuting Song 4 &
• Linlin Li 4  

Humanities and Social Sciences Communications volume  10 , Article number:  838 ( 2023 ) Cite this article

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• Science, technology and society

Online learning, as a pivotal element in modern education, is introducing fresh demands and challenges to the established teaching norms across various subjects. The adaptability of students to online learning and their sustained willingness to engage with it constitute two pivotal factors influencing the effective operation of online education systems. The dynamic relationship between these aspects may manifest unique traits within different academic disciplines, yet comprehensive research in this area remains notably scarce. In light of this, this study constructs an Adaptive Structural Learning and Technology Acceptance Model (ASL-TAM) with satisfaction towards online teaching as the mediating variable to investigate the impact and mechanism of online learning adaptivity on continuous usage intention for students from different disciplines. A total of 11,832 undergraduate students from 334 universities in 12 disciplinary categories in mainland China were selected, and structural equation modeling was used for analysis. The results showed that the ASL-TAM model could be fitted for all 12 disciplines. The perceived ease of use, perceived usefulness, and system environment adaptability dimensions of online learning adaptivity significantly and positively affect satisfaction towards online teaching and continuous usage intention. Satisfaction towards online teaching partially mediates the relationship between online learning adaptivity and continuous usage intention. There were significant differences in the results of the single-factor analysis of the observed variables for the 12 disciplines, and the path coefficients in the ASL-TAM model fitted for each discipline were also significantly different. Compared to the six disciplines under the science, technology, engineering, and mathematics (STEM) category, six disciplines under the humanities category exhibited more significant internal differences in the results of the single-factor analysis of perceived usefulness and the path coefficients for satisfaction towards online teaching. This research seeks to bridge existing research gaps and provide novel guidance and recommendations for the personalized design and distinctive implementation of online learning platforms and courses across various academic disciplines.

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Introduction.

With the rapid development of information technology, online learning has become an integral part of modern education. China possesses the largest scale of higher education system and online learning course system globally (National Bureau of Statistics ( 2020 )). However, despite the widespread adoption of online learning platforms, there remain controversies surrounding students’ engagement, satisfaction, and willingness to continue using them. Therefore, researching how to enhance students’ willingness to persist in using online learning platforms is of paramount importance for the development and promotion of online learning. In recent years, scholars have increasingly focused on the factor of students’ online learning adaptability when studying the effectiveness of online learning for students and their willingness to continue using online platforms.

Prior studies have indicated that the overall level of adaptability to online learning among college students is relatively low (Luo, Huang ( 2012 )), and adaptability often becomes a critical factor determining the quality of learning and academic assessment in an online learning environment (D’errico et al., ( 2018 )). However, there is currently insufficient research evidence to fully understand the specific mechanisms through which online learning adaptability affects willingness to persist in using online platforms, necessitating further empirical research.

Moreover, given the extensive use and profound influence of online learning technologies in diverse academic fields (Chikwa et al., ( 2015 )), alongside the marked disparities in online learning outcomes across these disciplines (Ieta et al., ( 2011 )), delving into the intricate interplay between students’ online learning adaptability and their inclination to persist in using these tools across various domains becomes particularly instructive. It can provide valuable insights for crafting precise and efficacious online learning strategies and pedagogical models aimed at enhancing student learning outcomes and bolstering students’ satisfaction with online education.

As such, this study aims to investigate the impact of online learning adaptability on the willingness to persist in using online platforms among students from different disciplines while exploring the potential mediating effect of their satisfaction towards online teaching. This study randomly selected 11,832 valid samples from 256,504 students attending online learning in 12 disciplines across 334 universities in mainland China. Using structural equation modeling, the study analyzed the comprehensive impact of students’ online learning adaptability on their continued use intention of online learning. The study also analyzed the possible mediating effects of satisfaction towards online teaching among the 12 disciplinary categories in the “Degree Granting and Talent Training Discipline Catalog” issued by the Ministry of Education of China.

Theoretical foundation and research hypotheses

Adaptive online learning and continuance intention and their influencing factors.

Continuous usage intention is originated from the tracking and evaluation of the continuous use of software programs. It refers to the user’s decision to continue using a software application and the frequency of use based on the overall perception of the application. Continuous usage intention is one of the most important user indicators for judging the software system’s life cycle. This study applies this factor in the context of online learning research, and forms the concept of “continuance intention of online learning,” which is defined as learners’ intentions to continue choosing online learning as the primary learning method. This study seeks to determine whether students are willing to continue using this type of learning after a certain period of time.

In contrast to Daumiller et al. ( 2021 ), who suggest that teachers’ goals and attitudes have a critical impact on students’ continuance intention to use online learning, Yao et al. ( 2022 ) believe that the key factor affecting continued use of online learning is students’ self-awareness, which is closely related to their adaptability to online learning. Online learning adaptability refers to students’ ability to adapt to the learning environment by adjusting their learning strategies and adopting adaptive behaviors when using online learning platforms or systems. In the 1980s, Davis ( 1986 ) drew on the Theory of Reasoned Action to propose the Technology Acceptance Model (TAM). TAM is primarily used to predict the extent to which individuals are inclined to accept, use, or reject new information technologies (Rogers, 2005 ). Given that online learning adaptability can help students overcome difficulties and challenges in the learning process, increasing their acceptance and depth of use of online learning, students’ adaptability to online learning platforms or systems is likely to be one of the important factors influencing their decision to continue using online platforms.

Online learning adaptability is a complex, multidimensional concept. Generally, it is considered the ability of students to adjust their learning strategies, behaviors, attitudes, goal setting, and resource utilization to adapt to new learning conditions and requirements (Kizilcec et al., 2015 ). This includes adaptability in areas such as technical proficiency, self-management skills, and information literacy. Among these, the adaptability of university students to online learning primarily depends on their familiarity with the technology tools they use. Therefore, mastering online learning platforms, social media, and digital tools can enhance students’ adaptability to online learning (Selwyn, 2011 ). Additionally, in terms of instructional design, the design of online courses has a significant impact on students’ adaptability. Clear learning objectives, organized content, and diverse teaching methods contribute to improving students’ adaptability (Picciano, 2017 ). Providing effective technical support and assistance channels can alleviate students’ technological difficulties and enhance their adaptability to online learning (Johnson & Adams, 2011 ).

In analyzing the issues of online learning adaptability and acceptance, TAM provides several foundational factors, such as perceived ease of use, perceived usefulness, satisfaction, and self-efficacy (Cakır, Solak ( 2015 )). Perceived usefulness and perceived ease of use are generally considered the two most essential variables (Martins et al., 2014 ). Perceived usefulness refers to the degree to which users believe that using a particular information technology enhances their work efficiency, while perceived ease of use refers to users’ perception of how easy it is to operate a specific information technology (Davis, 1989 ). Alharbi and Drew ( 2014 ) argue that perceived ease of use and perceived usefulness in the TAM model significantly positively influence students’ intentions to use online learning. Therefore, this study proposes the following hypotheses:

H1a: The perceived usefulness dimension of online learning adaptability has a positive significant impact on students’ continued usage intention.

H1b: The perceived ease of use dimension of online learning adaptability has a positive significant impact on students’ continued usage intention.

Apart from perceived ease of use and perceived usefulness, there is still no consensus on other important factors influencing continuance intention, especially regarding the strength and mechanisms of different factors (Joo et al., 2011 ). Liu et al. ( 2010 ) suggests that reasonable external extension variables can effectively predict users’ intentions to use online learning. Bazelais et al. ( 2018 ) and Xu, Lv ( 2022 ) also propose considering the additional effects of external influencing variables in the study of continuance intention. As a frontier and hot topic in online learning research (Jovanovic, Jovanovic ( 2015 )), the theory of Adaptive Learning Systems (ALS) from cognitive psychology proposes the concept of “human-machine interaction adaptability,” which includes two aspects: human adaptation to technology and technology adaptation to humans. The latter relies on the “learner model” to automatically analyze learners’ cognitive levels and learning styles, and then feedback to the former to enhance learners’ learning progress and effectiveness (Retalis, Papasalouros ( 2005 )). Social Cognitive Theory also suggests a similar viewpoint, indicating that students’ adaptability is largely influenced by multiple social contexts. A substantial amount of research on ALS also demonstrates that ALS, as a scientific learning medium, can more actively meet students’ learning needs (How, Hung ( 2019 )), help correct the learning paths generated by students’ autonomous learning habits (Nihad et al., ( 2017 )), and effectively improve students’ learning adaptability (Zulfiani et al., ( 2018 )). This study believes that online learning adaptability is a comprehensive, two-way process for students to adapt to changes in the learning environment through self-perception and for software systems to adapt to user needs systematically. It includes three variables: perceived ease of use, perceived usefulness, and system environment adaptability, with the latter referring to the functional adaptability of learning software systems to different learning styles of learners. Therefore, this study proposes the following hypothesis:

H1c: The system environment adaptation dimension of online learning adaptability has a positive significant impact on students’ continued usage intention.

Satisfaction towards online teaching and its possible mediating role

Prior research has suggested that satisfaction towards online teaching and perceived usefulness are considered core components in evaluating the effectiveness of online learning (Menon, Seow ( 2021 )), as they relate to the quality of online courses and students’ performance (Kuo et al., 2014 ). Scholars attach great importance to the research on the relationship between students’ satisfaction towards online teaching and their continued usage intention, with satisfaction being considered a key element affecting students’ continued usage intention and behavior (Lee, 2010 ).

Among the potential factors contributing to positive adaptability in online learning, perceived usefulness and perceived ease of use are recognized as two significant factors affecting satisfaction (Huang, 2020 ). Additionally, factors influencing satisfaction can indirectly impact the intention to continue using the system (Bhattacherjee, 2001 ). Furthermore, online educational platforms with robust system adaptability can provide a more stable network connection, higher-quality learning resources, and a more diverse array of learning pathways. Moreover, they can deliver personalized learning support and teaching resources tailored to individual student needs and learning characteristics. This assists students in overcoming learning challenges and enhances teaching effectiveness, ultimately leading to greater teaching satisfaction. Notably, technological innovations introduced by ALS effectively enhance learners’ perceived quality and have a positive indirect influence on teaching satisfaction (Janati et al., ( 2018 )). Therefore, the following hypotheses are proposed:

H2a: The perceived usefulness dimension of online learning adaptability positively and significantly affects students’ satisfaction towards online teaching.

H2b: The perceived ease of use dimension of online learning adaptability positively and significantly affects students’ satisfaction towards online teaching.

H2c: The system environment adaptation dimension of online learning adaptability positively and significantly affects students’ satisfaction towards online teaching.

It is generally believed that students’ satisfaction towards online teaching can refer to the indicator system proposed by the research on satisfaction towards classroom teaching, comprehensively evaluating common teaching factors such as course design, learning objectives, teaching methods, teacher qualifications, and interactive experiences. Palmer, Holt ( 2010 ) believe that the research on students’ satisfaction towards online teaching should pay more attention to the unique factors of the online teaching environment, such as teaching interactivity, technical proficiency, and online self-assessment. Bolliger and Wasilik ( 2009 ) also believes that we should start from the key participants in the online environment, focusing on the impact of various aspects such as teachers’ information technology application, students’ communication level, and school policy and logistical support. Kurucay and Inan ( 2017 ) opine that the key factor influencing online learning effectiveness is the interaction between learners. Regarding the main factors influencing learners’ satisfaction towards online teaching, Kranzow ( 2013 ) believe that the essential factors are related to teacher’s online course design level and the ability to respond to student needs in a timely manner. Hogan and McKnight ( 2007 ) believe that factors such as the teaching environment and technical support are the main reasons for influencing satisfaction towards online teaching. In addition, there are significant differences in the predicting factors for the acceptance of online learning and satisfaction towards online teaching among university students from different countries (Piccoli et al., 2001 ). Based on the above research, this study will further analyze the factors influencing learners’ satisfaction towards online teaching in the online learning environment, and propose the following hypothesis:

H3: Students’ satisfaction towards online teaching positively affects their continued usage intention.

Previous studies have shown that students’ satisfaction towards online teaching is likely to be influenced by their learning adaptability, and at the same time affects their intention to continue attending online learning (Waheed, 2010 ). Therefore, students’ satisfaction towards online teaching may play a special mediating role between students’ learning adaptability and their continuance intention. Yeung and Jordan ( 2007 ) found that factors such as perceived usefulness, perceived ease of use, and service quality evaluation that affect online learning satisfaction also have a positive impact on students’ continuance intention. Young ( 2013 ) reached similar conclusions and believed that students’ satisfaction towards online teaching plays a mediating role in the process of affecting their continuance intention. However, there are also different views about this topic. For example, Troshani et al. ( 2011 ) found that although perceived ease of use has a significant impact on learners’ usage satisfaction, it does not have a significant impact on their continuance intention. Therefore, the mediating effect of learning adaptability on learners’ continuance intention may be extremely important and needs to be verified through empirical research. Therefore, this study proposes that students’ satisfaction towards online teaching plays a mediating role between their online learning adaptability and continued usage intention. The specific hypotheses are as follows.

H4a: Students’ satisfaction towards online teaching plays a mediating role between perceived usefulness and their continued usage intention.

H4b: Students’ satisfaction towards online teaching plays a mediating role between perceived ease of use and their continued usage intention.

H4c: Students’ satisfaction towards online teaching plays a mediating role between system environment adaptability and their continued usage intention.

Designing the model framework

As mentioned earlier, it is feasible to use the TAM model to study the sustained usage intention of online learning, and its explanatory power has been verified by empirical studies (Dziuban et al., 2013 ). However, with the increasing complexity of the online environment, the traditional TAM model may encounter issues with low reliability and validity in explaining complex user environments. Therefore, the academic community has been continuously selecting, combining, and adjusting the basic components of the TAM model. Davis et al. ( 1992 ) pointed out that when using TAM theory, multiple external variables, including intrinsic motivation, should be considered, as they may have complex effects on endogenous variables and behavioral intentions. Farahat ( 2012 ) found that, in addition to perceived usefulness and perceived ease of use, student attitudes and social influences in online learning are also important factors that influence students’ willingness to engage in online learning. Therefore, based on the Technology Acceptance Model (TAM) and the Adaptive Structural Learning Model (ALS), this study combines them to construct the Adaptive Learning and Technology Acceptance Model (ASL-TAM model; see Fig. 1 ) as follows:

In ASL-TAM model, online learning adaptability consists of three factors, which are hypothesized to predict continued usage intention and satisfaction towards online teaching.

Methodology

Data source.

The data for this study were collected from an online learning survey conducted by a Teacher Development Centre of a public university (IRB No. NB-HEC-20200328L) in mainland China from 2020 to 2021. The survey was distributed to students through the academic affairs offices of various schools. Additionally, two lie-detection questions were included in the questionnaire to ensure the validity and reliability of the data. Each student account could only save one survey form. In other words, if the same account answered multiple times, the results of the last response would automatically overwrite the previous ones. A total of 256,504 data sets were collected from 334 universities. Among the surveyed students, there were 110,411 males (43%) and 146,093 females (57%). In terms of geographical distribution, 110,919 students (43.2%) were from the eastern region of China, 106,007 (41.3%) were from the central region, and 38,847 (15.1%) were from the western region. The surveyed students were also classified into different academic disciplines, including 11,086 in philosophy, 20,953 in economics, 7420 in law, 17,100 in education, 24,658 in literature, 1201 in history, 29,517 in natural science, 76,301 in engineering, 5295 in agriculture, 11,161 in medicine, 24,583 in management, and 27,229 in arts. A sample of 1000 student questionnaires was randomly selected from each academic discipline, resulting in a total of 12,000 data sets. The sample was cleaned based on criteria such as lie-detection questions, response times (data below 5 min or above 20 min were removed based on the statistical “3σ rule”), age (data below 15 years old or above 25 years old were removed based on the statistical “3σ rule”), school names (data with randomly filled school names were removed), and whether online learning was used (data indicating no usage were removed). In total, 162 samples were cleaned, resulting in 11,832 valid samples (with 986 for each of the 12 academic disciplines).

Instrumentation

This study was conceptualized based on TAM from the theory of rational behavior and the ALS theory from cognitive psychology. These theories were employed to investigate the underlying mechanisms of the impact of online learning adaptability on users’ continuance intention. In this regard, we consulted the research findings of scholars such as Davis ( 1993 ), Igbaria ( 1990 ), Ajzen & Fishbein ( 1980 ), Chen and Tseng ( 2012 ), among others. The questionnaire consisted of 33 items measuring five variables (see Table S1 for the complete questionnaire): perceived usefulness (11 items), perceived ease of use (3 items), adjustment to system environments (10 items), satisfaction of teaching (7 items), and continuance intention (2 items). The overall reliability of the questionnaire was tested using the Cronbach’s alpha coefficient (0.924), KMO (0.937), and Bartlett’s sphericity test ( p  < 0.001) in SPSS 25.0 software, indicating that the questionnaire data were reliable and suitable for exploratory factor analysis (EFA). Three principal components were extracted for perceived usefulness (PU): teaching resources (PU_TR), classroom teaching (PU_CT), and teaching evaluation (PU_TE). Three principal components were also extracted for perceived ease of use (PEU): technical training (PEU_TT), pedagogical training (PEU_PT), and proficiency levels (PEU_PL). Three principal components were extracted for system environment adaptation (SEA): technical service (SEA_TSER), teaching support (SEA_TSUP), and policy support (SEA_PS). Three principal components were extracted for satisfaction with online teaching (ST): effectiveness of teaching (ST_TE), teaching experience (ST_TEXP), and learning outcomes (ST_LO). Two principal components were extracted for continuance intention (CIN): online mode (CIN_ON) and blended mode (CIN_BL). Perceived usefulness, perceived ease of use, and system environment adaptation were combined to form the independent variable “adaptive structural learning (ASL)” in this study, while satisfaction towards online teaching was the hypothesized mediating variable and continuance intention was the dependent variable. The academic disciplines were treated as control variables. The perceived usefulness and perceived ease of use scales were adapted from Davis ( 1993 ), the system environment adaptation scale was adapted from Igbaria ( 1990 ), the satisfaction towards online teaching scale was adapted from Ajzen and Fishbein ( 1980 ), and the continuance intention scale was adapted from Chen and Tseng ( 2012 ).

Research method

Descriptive statistics were conducted on the data of 12 disciplines using SPSS 25.0 software, and model construction, model revision, and model interpretation were carried out using AMOS 24.0.

Reliability analysis

Reliability analysis was conducted on the 14 latent variables across the 12 disciplines using SPSS 25.0 software (see Table 1 for results). The results showed that the alpha values of the observation variables based on standardized items were all greater than or equal to 0.9, indicating that the questionnaire of the 12 disciplines had high reliability. During reliability analysis, the scores of the latent variables calculated using the mean method also had considerable reliability, indicating excellent data reliability. The data of the 12 disciplines were suitable for further structural model testing.

Common method bias (CMB) test

The data used in this study were collected through self-reporting methods on the internet, which may have CMB. Before formal data analysis, a Harman single-factor test was conducted to examine common method bias. First, exploratory factor analysis (unrotated) was performed using SPSS 25.0 software. The results showed that the first principal component accounted for 29.21% of the variance, which did not meet the 40% threshold.

One-way ANOVA of disciplinary variables

One-way ANOVA analysis was conducted on the observation variables of 12 disciplines. According to the results in Table 2 , if the 12 disciplines are viewed as a whole, the evaluation of perceived ease of use (3.62) is higher than system environment adaptation (3.60) and perceived usefulness (3.47). The satisfaction towards online teaching (3.47) is higher than continuous usage intention (3.44). Perceived usefulness is the main weak link of online learning adaptability, and the main observation variable that causes the low value of perceived usefulness is teaching evaluation (3.26). The lowest discipline evaluation value comes from philosophy (3.41). The observation variable with the lowest evaluation value in perceived ease of use is technical training (3.58), and the observation variable with the lowest evaluation value in system environment adaptation is technical service (3.53). The observation variable with the lowest evaluation value in the satisfaction towards online teaching is effectiveness of teaching (3.28). All 14 observation variables of the 12 disciplines showed significant inter-group differences ( p  < 0.001), indicating that there were general differences in the evaluation outcomes among the observation variables of different disciplines.

Correlation analysis among variables

To explore the relationships between the variables, a correlation analysis was performed. As shown in Table 3 , there were significant positive correlations ( p  < 0.001) between the variables of perceived usefulness, perceived ease of use, and system environment adaptation. There were also significant positive correlations ( p  < 0.001) between the variables of perceived usefulness, perceived ease of use, and system environment adaptation with the mediating variables of satisfaction towards online teaching and continued usage intention. Additionally, there was a significant positive correlation ( p  < 0.001) between satisfaction towards online teaching and continued usage intention.

Model construction and fitting

Based on the ASL-TAM model developed in Fig. 1 , a structural equation model was constructed using AMOS 24.0 software, and the initial model was estimated using maximum likelihood. Taking the subject of physics as an example, the results of the initial model fit showed that the correction index MI value of the residual path [e2 < -->e3] was relatively large. Therefore, the initial model was corrected by adding the [e2 < -->e3] residual path, and all path p -values were less than 0.05 after the correction, indicating statistical significance. The fitted model is shown in Fig. 2 .

The validated ASL-TAM model for the subject of physics demonstrated good fit, with most hypotheses being substantiated.

The fitted model for the subject of physics showed good results. The same fitting method was used for the other 11 subjects, and the results showed that all 12 models could be fitted, and the 12 fitting goodness-of-fit indices were within the standard range. Therefore, the ASL-TAM model can be used for relevant evaluation and prediction work (see Table 4 for goodness-of-fit indices).

Path analysis results of fitted models

The path coefficients of the structural equation can reflect the mutual relationships between latent variables and between latent variables and observed variables. The path coefficients between variables after the fitting of the 12 subjects are shown in Table 5 . First, the ASL-TAM models of all 12 subjects can achieve overall convergence. The path coefficients of satisfaction towards online teaching (ST) on continuous usage intention (CIN) are all significant in all 12 subjects, verifying research hypothesis H3. Second, the three paths “perceived ease of use (PEU) → continuous usage intention (CIN)”, “perceived usefulness (PU) → continuous usage intention (CIN)”, and “system environment adaptation (SEA) → continuous usage intention (CIN)” all display significant path coefficients and can be fitted into the ASL-TAM model, indicating that online learning adaptability and its three dimensions all have a significant positive impact on continuous usage intention (CIN), substantiating research hypotheses H1, H1a, H1b, and H1c. Third, the three paths “perceived ease of use (PEU) → satisfaction towards online teaching (ST)”, “perceived usefulness (PU) → satisfaction towards online teaching (ST)”, and “system environment adaptation (SEA) → satisfaction towards online teaching (ST)” all display significant path coefficients, indicating that online learning adaptability and its three dimensions all have a significant positive impact on satisfaction towards online teaching (ST), verifying research hypotheses H2, H2a, H2b, and H2c. Additionally, the path “Satisfaction towards online teaching (ST) → continuous usage intention (CIN)” is displayed with a significant path coefficient in all 12 subjects, indicating that “satisfaction towards online teaching (ST)” has a partial mediating effect between “perceived ease of use (PEU)”, “perceived usefulness (PU)”, “system environment adaptation (SEA)” and “continuous usage intention (CIN)”, verifying research hypotheses H4, H4a, H4b, and H4c.

This study confirms the positive impact of online learning adaptability on users’ intention to continue using the platform. This aligns with previous research findings that students’ adaptation to a course significantly affects their learning outcomes (Manwaring et al., 2017 ). Unlike most studies that only focus on students’ one-way adaptation to the teaching system, this study confirms that both students’ “perceived adaptation” to the system and the system’s “adaptive needs” to the students are equally important and should be considered as a whole. When students’ perceived position in the system matches the target characteristics predicted by the system, they will rate the teaching activities higher (Bretschneider et al., 2012 ).

This study also confirms the positive impact of online learning adaptability on satisfaction towards online teaching, which is in line with previous research that adaptability is an important indicator of students’ learning satisfaction, perceived utility, and intention to continue learning (Machado, Meirelles ( 2015 )). Therefore, adaptability should be the logical starting point for designing online learning systems. At the same time, enhancing the intelligence perception of “human-computer interaction” and improving the teaching adaptivity of “teacher-student interaction” are important directions for enhancing users’ intention to continue using online learning and improving the overall quality of online learning.

This study also confirms the positive impact of satisfaction towards online teaching on users’ intention to continue using the platform, and the TAM model is applicable in evaluating satisfaction and intention to continue using in 12 subject areas. The adaptive structural learning and technology acceptance model fit successfully in all 12 subject areas. This confirms that the TAM model can be used to explain the factors that influence learners’ acceptance of online learning (Venkatesh, Davis ( 2000 )), and the core structure of TAM has a significant impact on users’ intention to continue using (Natasia et al., 2022 ).

Furthermore, this study confirms that satisfaction towards online teaching partially mediates the relationship between online learning adaptability and users’ intention to continue using the platform. The ASL-TAM model developed in this study reveals that there are expression differences in the factors that affect satisfaction towards online teaching and users’ intention to continue using in the 12 subject areas, and the ASL-TAM model can explore the deep path reasons for the expression differences in the factors affecting users’ intention to continue using (Al-Azawei, Lundqvist ( 2015 )), and then analyze the educational goals and methods paths for implementing online learning in different subjects.

This study has three contributions. First, the study found that perceived usefulness (PU) (3.47) was lower than system environment adaptation (SEA) (3.60) and perceived ease of use (PEU) (3.62). The continuous usage intention (CIN) (3.44) was lower than satisfaction towards online teaching (ST) (3.47). The main observed variables leading to a low evaluation of perceived usefulness (PU) were teaching evaluation (PU_TE) (3.26) while the lowest evaluated variable in perceived ease of use (PEU) was technology training (PEU_TT) (3.58). In system environment adaptation (SEA), the lowest evaluated variable was technical service (SEA_TSER) (3.53) while the lowest evaluated variable in satisfaction towards online teaching (ST) was teaching effectiveness (ST_TE) (3.28). This indicates that online education in mainland China is still in the early stage of hardware facilities configuration and teaching technology training. The continuous usage intention (CIN) is generally weak, possibly due to the weak links in the early adaptation to online learning, which affects the evaluation of satisfaction towards online teaching (ST), leading to a weaker overall continuous usage intention (CIN). Online learning needs more specific and effective project support (Ramadhan et al., 2021 ).

Second, the study confirms that satisfaction towards online teaching (ST) plays a partial or complete mediating effect between perceived ease of use (PEU), perceived usefulness (PU), system environment adaptation (SEA) and continuous usage intention (CIN), which confirms previous research conclusions. That is, user satisfaction is a key antecedent to influence user intention to continue use and behavior (Igbaria et al., 1997 ). There are many possible factors that influence continuous usage intention (CIN) of a teaching method, but among various factors, satisfaction towards online teaching (ST) of the student population is the “central factor”, especially for online education, learner satisfaction is considered a key factor for teaching success (Joo et al., 2011 ). It is also important to strengthen system environment adaptation (SEA) based on human-computer interaction, as online learning requires an attractive and motivational external environment (Agyeiwaah et al., 2022 ), and satisfaction may vary due to internet experience (Reed, 2001 ).

Thirdly, this study confirms the significant differences in satisfaction towards online teaching (ST) and continuous usage intention (CIN) between STEM and humanities disciplines. Influenced by the early college entrance examination system, China has conventionally classified disciplines into STEM and humanities, similar to the “arts” and “science” branches in the subject guidelines of Western universities. The classification not only affects the disciplines but also results in significant differences in academic literacy among students in different fields. This study found that compared to STEM disciplines (such as natural science, engineering, agriculture, medicine, and management), the six traditional humanities disciplines, namely philosophy, law, education, literature, history, and economics, showed extremely significant differences in perceived usefulness (PU), which may be due to the difference in teaching style between humanities and STEM (Tuimur et al., 2012 ) and the peer cultural influence within the humanities. A study of nearly 500,000 online courses in the state of Washington in the United States has similar conclusions that students face greater difficulties in online learning in fields like English and social sciences, possibly due to the existence of “negative peer effects” in the online courses of these disciplines (Lv et al., 2022 ).

Implications

In order to enhance the satisfaction towards online teaching and continued usage intention of online education, this study proposes the following suggestions:

From the perspective of cognitive psychology, the differences in online teaching among different disciplines are mainly manifested in various aspects such as the cognitive perspectives and learning habits of students with different disciplinary backgrounds. From the standpoint of educational technology theory, there is a need for continuous development of multidimensional and multilevel teaching systems to adapt to the knowledge structures, teaching principles, and curriculum characteristics of different disciplines. Furthermore, constructivist learning theory emphasizes that teachers should assist students in improving their learning adaptability more actively and in constructing knowledge and meaning more proactively. This study empirically validates the above viewpoints and provides new discoveries. Research shows that there are significant differences in satisfaction towards online teaching and continued usage intention in online learning among different subjects, so different online learning for different subjects should be implemented. On the one hand, the convergence of online learning in different subjects should be grasped, and a wide-caliber, widely applicable teaching platform carrier should be constructed to effectively integrate different subject knowledge into the virtual classroom knowledge situation, and better promote the integration of knowledge and skills. On the other hand, attention should be paid to the objective differences of different subjects, and an online education system reflecting the advantages of different subjects should be designed according to the teaching contents of different subjects.

From the perspective of practicality, it is necessary to pay close attention to the significant differences among various disciplines in terms of subject content and learning objectives, teaching methods and learning activities, assessment and feedback methods, as well as the roles of teachers and technological support. It is important to actively develop teaching methods that are tailored to different disciplines, especially in the case of experimental courses. Compared with traditional classroom education, the important breakthrough of online learning is the more convenient and timely teaching feedback. Future online learning systems should create adaptive learning environments based on the different characteristics of learners (Park and Lee, 2003 ), and accelerate the construction of adaptive learning systems for college students with different learning methods in different subjects, which is an effective solution to the conflict between diversified subject needs and static teaching resources, and an important way to resolve the contradiction between diversified student levels and limited teaching resources. For science and engineering subjects, attention should be paid to improving the external environment of online learning, actively improving online learning performance evaluation, promoting industry-university-research cooperation, promoting demand docking, resource sharing, and complementary advantages, promoting industry-education integration and industry-university co-construction, and achieving win-win results for teachers and students. For humanities subjects, the technical support for each link of online learning should be improved, and more humanistic care should be reflected in interactive teaching support. Through more social integration, knowledge exploration-based social consultation can be promoted.

In terms of the broader external educational environment and technological development trends, we should emphasize the opportunities for educational technology innovation and industry-education integration brought about by the differential development of online teaching in various disciplines. Clearly, the issue of disciplinary differences presents challenges in terms of teaching organization and operation, but it also promotes opportunities for personalized learning, collaborative teaching, and diversified assessment. China is already a major player in online education, but it is not yet a powerhouse in this field. To unleash the educational value of online learning and expand its innovative significance, online education, represented by flipped classrooms and MOOCs, not only provides new teaching methods and educational pathways, but also brings innovative educational ideas and paradigms. Therefore, online education needs to emphasize the re-examination of external contexts, overcome the mechanical thinking of “100% replication of classroom education,” and explore new teaching paths and operating modes, providing teachers and students with more novel teaching experiences and promoting the comprehensive improvement of their knowledge, abilities, and qualities.

Limitations and future research

This study has two limitations. Firstly, to increase the credibility of the research conclusions, we have tried to increase the sample size, resulting in a relatively large number of universities involved in the study. These universities may have differences in their discipline settings and standards, which may introduce some errors that need to be addressed in future research. Secondly, previous studies have shown that factors such as the location of the participants, the level of their universities, and their academic year may affect their satisfaction with teaching. We were unable to eliminate these possible interferences in this study and will improve this in future research.

Data availability

The data presented in this study are available on request from the corresponding author. According to the regulation of the Ethics Committee of Ningbo University, the data are not publicly available due to ethical reasons as they contain personally identifiable information.

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Acknowledgements

This research was funded by the National Social Science Foundation (Education) Project, “Research on the Path and Mechanism of Universities Promoting Rural Entrepreneurship Education under the Background of Rural Revitalization” (grant No. BIA200204).

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Li, Z., Lou, X., Chen, M. et al. Students’ online learning adaptability and their continuous usage intention across different disciplines. Humanit Soc Sci Commun 10 , 838 (2023). https://doi.org/10.1057/s41599-023-02376-5

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A systematic review of research on online teaching and learning from 2009 to 2018

Associated data.

Systematic reviews were conducted in the nineties and early 2000's on online learning research. However, there is no review examining the broader aspect of research themes in online learning in the last decade. This systematic review addresses this gap by examining 619 research articles on online learning published in twelve journals in the last decade. These studies were examined for publication trends and patterns, research themes, research methods, and research settings and compared with the research themes from the previous decades. While there has been a slight decrease in the number of studies on online learning in 2015 and 2016, it has then continued to increase in 2017 and 2018. The majority of the studies were quantitative in nature and were examined in higher education. Online learning research was categorized into twelve themes and a framework across learner, course and instructor, and organizational levels was developed. Online learner characteristics and online engagement were examined in a high number of studies and were consistent with three of the prior systematic reviews. However, there is still a need for more research on organization level topics such as leadership, policy, and management and access, culture, equity, inclusion, and ethics and also on online instructor characteristics.

• • Twelve online learning research themes were identified in 2009–2018.
• • A framework with learner, course and instructor, and organizational levels was used.
• • Online learner characteristics and engagement were the mostly examined themes.
• • The majority of the studies used quantitative research methods and in higher education.
• • There is a need for more research on organization level topics.

1. Introduction

Online learning has been on the increase in the last two decades. In the United States, though higher education enrollment has declined, online learning enrollment in public institutions has continued to increase ( Allen & Seaman, 2017 ), and so has the research on online learning. There have been review studies conducted on specific areas on online learning such as innovations in online learning strategies ( Davis et al., 2018 ), empirical MOOC literature ( Liyanagunawardena et al., 2013 ; Veletsianos & Shepherdson, 2016 ; Zhu et al., 2018 ), quality in online education ( Esfijani, 2018 ), accessibility in online higher education ( Lee, 2017 ), synchronous online learning ( Martin et al., 2017 ), K-12 preparation for online teaching ( Moore-Adams et al., 2016 ), polychronicity in online learning ( Capdeferro et al., 2014 ), meaningful learning research in elearning and online learning environments ( Tsai, Shen, & Chiang, 2013 ), problem-based learning in elearning and online learning environments ( Tsai & Chiang, 2013 ), asynchronous online discussions ( Thomas, 2013 ), self-regulated learning in online learning environments ( Tsai, Shen, & Fan, 2013 ), game-based learning in online learning environments ( Tsai & Fan, 2013 ), and online course dropout ( Lee & Choi, 2011 ). While there have been review studies conducted on specific online learning topics, very few studies have been conducted on the broader aspect of online learning examining research themes.

2. Systematic Reviews of Distance Education and Online Learning Research

Distance education has evolved from offline to online settings with the access to internet and COVID-19 has made online learning the common delivery method across the world. Tallent-Runnels et al. (2006) reviewed research late 1990's to early 2000's, Berge and Mrozowski (2001) reviewed research 1990 to 1999, and Zawacki-Richter et al. (2009) reviewed research in 2000–2008 on distance education and online learning. Table 1 shows the research themes from previous systematic reviews on online learning research. There are some themes that re-occur in the various reviews, and there are also new themes that emerge. Though there have been reviews conducted in the nineties and early 2000's, there is no review examining the broader aspect of research themes in online learning in the last decade. Hence, the need for this systematic review which informs the research themes in online learning from 2009 to 2018. In the following sections, we review these systematic review studies in detail.

Comparison of online learning research themes from previous studies.

2.1. Distance education research themes, 1990 to 1999 ( Berge & Mrozowski, 2001 )

Berge and Mrozowski (2001) reviewed 890 research articles and dissertation abstracts on distance education from 1990 to 1999. The four distance education journals chosen by the authors to represent distance education included, American Journal of Distance Education, Distance Education, Open Learning, and the Journal of Distance Education. This review overlapped in the dates of the Tallent-Runnels et al. (2006) study. Berge and Mrozowski (2001) categorized the articles according to Sherry's (1996) ten themes of research issues in distance education: redefining roles of instructor and students, technologies used, issues of design, strategies to stimulate learning, learner characteristics and support, issues related to operating and policies and administration, access and equity, and costs and benefits.

In the Berge and Mrozowski (2001) study, more than 100 studies focused on each of the three themes: (1) design issues, (2) learner characteristics, and (3) strategies to increase interactivity and active learning. By design issues, the authors focused on instructional systems design and focused on topics such as content requirement, technical constraints, interactivity, and feedback. The next theme, strategies to increase interactivity and active learning, were closely related to design issues and focused on students’ modes of learning. Learner characteristics focused on accommodating various learning styles through customized instructional theory. Less than 50 studies focused on the three least examined themes: (1) cost-benefit tradeoffs, (2) equity and accessibility, and (3) learner support. Cost-benefit trade-offs focused on the implementation costs of distance education based on school characteristics. Equity and accessibility focused on the equity of access to distance education systems. Learner support included topics such as teacher to teacher support as well as teacher to student support.

2.2. Online learning research themes, 1993 to 2004 ( Tallent-Runnels et al., 2006 )

Tallent-Runnels et al. (2006) reviewed research on online instruction from 1993 to 2004. They reviewed 76 articles focused on online learning by searching five databases, ERIC, PsycINFO, ContentFirst, Education Abstracts, and WilsonSelect. Tallent-Runnels et al. (2006) categorized research into four themes, (1) course environment, (2) learners' outcomes, (3) learners’ characteristics, and (4) institutional and administrative factors. The first theme that the authors describe as course environment ( n  = 41, 53.9%) is an overarching theme that includes classroom culture, structural assistance, success factors, online interaction, and evaluation.

Tallent-Runnels et al. (2006) for their second theme found that studies focused on questions involving the process of teaching and learning and methods to explore cognitive and affective learner outcomes ( n  = 29, 38.2%). The authors stated that they found the research designs flawed and lacked rigor. However, the literature comparing traditional and online classrooms found both delivery systems to be adequate. Another research theme focused on learners’ characteristics ( n  = 12, 15.8%) and the synergy of learners, design of the online course, and system of delivery. Research findings revealed that online learners were mainly non-traditional, Caucasian, had different learning styles, and were highly motivated to learn. The final theme that they reported was institutional and administrative factors (n  = 13, 17.1%) on online learning. Their findings revealed that there was a lack of scholarly research in this area and most institutions did not have formal policies in place for course development as well as faculty and student support in training and evaluation. Their research confirmed that when universities offered online courses, it improved student enrollment numbers.

2.3. Distance education research themes 2000 to 2008 ( Zawacki-Richter et al., 2009 )

Zawacki-Richter et al. (2009) reviewed 695 articles on distance education from 2000 to 2008 using the Delphi method for consensus in identifying areas and classified the literature from five prominent journals. The five journals selected due to their wide scope in research in distance education included Open Learning, Distance Education, American Journal of Distance Education, the Journal of Distance Education, and the International Review of Research in Open and Distributed Learning. The reviewers examined the main focus of research and identified gaps in distance education research in this review.

Zawacki-Richter et al. (2009) classified the studies into macro, meso and micro levels focusing on 15 areas of research. The five areas of the macro-level addressed: (1) access, equity and ethics to deliver distance education for developing nations and the role of various technologies to narrow the digital divide, (2) teaching and learning drivers, markets, and professional development in the global context, (3) distance delivery systems and institutional partnerships and programs and impact of hybrid modes of delivery, (4) theoretical frameworks and models for instruction, knowledge building, and learner interactions in distance education practice, and (5) the types of preferred research methodologies. The meso-level focused on seven areas that involve: (1) management and organization for sustaining distance education programs, (2) examining financial aspects of developing and implementing online programs, (3) the challenges and benefits of new technologies for teaching and learning, (4) incentives to innovate, (5) professional development and support for faculty, (6) learner support services, and (7) issues involving quality standards and the impact on student enrollment and retention. The micro-level focused on three areas: (1) instructional design and pedagogical approaches, (2) culturally appropriate materials, interaction, communication, and collaboration among a community of learners, and (3) focus on characteristics of adult learners, socio-economic backgrounds, learning preferences, and dispositions.

The top three research themes in this review by Zawacki-Richter et al. (2009) were interaction and communities of learning ( n  = 122, 17.6%), instructional design ( n  = 121, 17.4%) and learner characteristics ( n  = 113, 16.3%). The lowest number of studies (less than 3%) were found in studies examining the following research themes, management and organization ( n  = 18), research methods in DE and knowledge transfer ( n  = 13), globalization of education and cross-cultural aspects ( n  = 13), innovation and change ( n  = 13), and costs and benefits ( n  = 12).

2.4. Online learning research themes

These three systematic reviews provide a broad understanding of distance education and online learning research themes from 1990 to 2008. However, there is an increase in the number of research studies on online learning in this decade and there is a need to identify recent research themes examined. Based on the previous systematic reviews ( Berge & Mrozowski, 2001 ; Hung, 2012 ; Tallent-Runnels et al., 2006 ; Zawacki-Richter et al., 2009 ), online learning research in this study is grouped into twelve different research themes which include Learner characteristics, Instructor characteristics, Course or program design and development, Course Facilitation, Engagement, Course Assessment, Course Technologies, Access, Culture, Equity, Inclusion, and Ethics, Leadership, Policy and Management, Instructor and Learner Support, and Learner Outcomes. Table 2 below describes each of the research themes and using these themes, a framework is derived in Fig. 1 .

Research themes in online learning.

Online learning research themes framework.

The collection of research themes is presented as a framework in Fig. 1 . The themes are organized by domain or level to underscore the nested relationship that exists. As evidenced by the assortment of themes, research can focus on any domain of delivery or associated context. The “Learner” domain captures characteristics and outcomes related to learners and their interaction within the courses. The “Course and Instructor” domain captures elements about the broader design of the course and facilitation by the instructor, and the “Organizational” domain acknowledges the contextual influences on the course. It is important to note as well that due to the nesting, research themes can cross domains. For example, the broader cultural context may be studied as it pertains to course design and development, and institutional support can include both learner support and instructor support. Likewise, engagement research can involve instructors as well as learners.

In this introduction section, we have reviewed three systematic reviews on online learning research ( Berge & Mrozowski, 2001 ; Tallent-Runnels et al., 2006 ; Zawacki-Richter et al., 2009 ). Based on these reviews and other research, we have derived twelve themes to develop an online learning research framework which is nested in three levels: learner, course and instructor, and organization.

2.5. Purpose of this research

In two out of the three previous reviews, design, learner characteristics and interaction were examined in the highest number of studies. On the other hand, cost-benefit tradeoffs, equity and accessibility, institutional and administrative factors, and globalization and cross-cultural aspects were examined in the least number of studies. One explanation for this may be that it is a function of nesting, noting that studies falling in the Organizational and Course levels may encompass several courses or many more participants within courses. However, while some research themes re-occur, there are also variations in some themes across time, suggesting the importance of research themes rise and fall over time. Thus, a critical examination of the trends in themes is helpful for understanding where research is needed most. Also, since there is no recent study examining online learning research themes in the last decade, this study strives to address that gap by focusing on recent research themes found in the literature, and also reviewing research methods and settings. Notably, one goal is to also compare findings from this decade to the previous review studies. Overall, the purpose of this study is to examine publication trends in online learning research taking place during the last ten years and compare it with the previous themes identified in other review studies. Due to the continued growth of online learning research into new contexts and among new researchers, we also examine the research methods and settings found in the studies of this review.

The following research questions are addressed in this study.

• 1. What percentage of the population of articles published in the journals reviewed from 2009 to 2018 were related to online learning and empirical?
• 2. What is the frequency of online learning research themes in the empirical online learning articles of journals reviewed from 2009 to 2018?
• 3. What is the frequency of research methods and settings that researchers employed in the empirical online learning articles of the journals reviewed from 2009 to 2018?

This five-step systematic review process described in the U.S. Department of Education, Institute of Education Sciences, What Works Clearinghouse Procedures and Standards Handbook, Version 4.0 ( 2017 ) was used in this systematic review: (a) developing the review protocol, (b) identifying relevant literature, (c) screening studies, (d) reviewing articles, and (e) reporting findings.

3.1. Data sources and search strategies

The Education Research Complete database was searched using the keywords below for published articles between the years 2009 and 2018 using both the Title and Keyword function for the following search terms.

“online learning" OR "online teaching" OR "online program" OR "online course" OR “online education”

3.2. Inclusion/exclusion criteria

The initial search of online learning research among journals in the database resulted in more than 3000 possible articles. Therefore, we limited our search to select journals that focus on publishing peer-reviewed online learning and educational research. Our aim was to capture the journals that published the most articles in online learning. However, we also wanted to incorporate the concept of rigor, so we used expert perception to identify 12 peer-reviewed journals that publish high-quality online learning research. Dissertations and conference proceedings were excluded. To be included in this systematic review, each study had to meet the screening criteria as described in Table 3 . A research study was excluded if it did not meet all of the criteria to be included.

Inclusion/Exclusion criteria.

3.3. Process flow selection of articles

Fig. 2 shows the process flow involved in the selection of articles. The search in the database Education Research Complete yielded an initial sample of 3332 articles. Targeting the 12 journals removed 2579 articles. After reviewing the abstracts, we removed 134 articles based on the inclusion/exclusion criteria. The final sample, consisting of 619 articles, was entered into the computer software MAXQDA ( VERBI Software, 2019 ) for coding.

Flowchart of online learning research selection.

3.4. Developing review protocol

A review protocol was designed as a codebook in MAXQDA ( VERBI Software, 2019 ) by the three researchers. The codebook was developed based on findings from the previous review studies and from the initial screening of the articles in this review. The codebook included 12 research themes listed earlier in Table 2 (Learner characteristics, Instructor characteristics, Course or program design and development, Course Facilitation, Engagement, Course Assessment, Course Technologies, Access, Culture, Equity, Inclusion, and Ethics, Leadership, Policy and Management, Instructor and Learner Support, and Learner Outcomes), four research settings (higher education, continuing education, K-12, corporate/military), and three research designs (quantitative, qualitative and mixed methods). Fig. 3 below is a screenshot of MAXQDA used for the coding process.

Codebook from MAXQDA.

3.5. Data coding

Research articles were coded by two researchers in MAXQDA. Two researchers independently coded 10% of the articles and then discussed and updated the coding framework. The second author who was a doctoral student coded the remaining studies. The researchers met bi-weekly to address coding questions that emerged. After the first phase of coding, we found that more than 100 studies fell into each of the categories of Learner Characteristics or Engagement, so we decided to pursue a second phase of coding and reexamine the two themes. Learner Characteristics were classified into the subthemes of Academic, Affective, Motivational, Self-regulation, Cognitive, and Demographic Characteristics. Engagement was classified into the subthemes of Collaborating, Communication, Community, Involvement, Interaction, Participation, and Presence.

3.6. Data analysis

Frequency tables were generated for each of the variables so that outliers could be examined and narrative data could be collapsed into categories. Once cleaned and collapsed into a reasonable number of categories, descriptive statistics were used to describe each of the coded elements. We first present the frequencies of publications related to online learning in the 12 journals. The total number of articles for each journal (collectively, the population) was hand-counted from journal websites, excluding editorials and book reviews. The publication trend of online learning research was also depicted from 2009 to 2018. Then, the descriptive information of the 12 themes, including the subthemes of Learner Characteristics and Engagement were provided. Finally, research themes by research settings and methodology were elaborated.

4.1. Publication trends on online learning

Publication patterns of the 619 articles reviewed from the 12 journals are presented in Table 4 . International Review of Research in Open and Distributed Learning had the highest number of publications in this review. Overall, about 8% of the articles appearing in these twelve journals consisted of online learning publications; however, several journals had concentrations of online learning articles totaling more than 20%.

Empirical online learning research articles by journal, 2009–2018.

Note . Journal's Total Article count excludes reviews and editorials.

The publication trend of online learning research is depicted in Fig. 4 . When disaggregated by year, the total frequency of publications shows an increasing trend. Online learning articles increased throughout the decade and hit a relative maximum in 2014. The greatest number of online learning articles ( n  = 86) occurred most recently, in 2018.

Online learning publication trends by year.

4.2. Online learning research themes that appeared in the selected articles

The publications were categorized into the twelve research themes identified in Fig. 1 . The frequency counts and percentages of the research themes are provided in Table 5 below. A majority of the research is categorized into the Learner domain. The fewest number of articles appears in the Organization domain.

Research themes in the online learning publications from 2009 to 2018.

The specific themes of Engagement ( n  = 179, 28.92%) and Learner Characteristics ( n  = 134, 21.65%) were most often examined in publications. These two themes were further coded to identify sub-themes, which are described in the next two sections. Publications focusing on Instructor Characteristics ( n  = 21, 3.39%) were least common in the dataset.

4.2.1. Research on engagement

The largest number of studies was on engagement in online learning, which in the online learning literature is referred to and examined through different terms. Hence, we explore this category in more detail. In this review, we categorized the articles into seven different sub-themes as examined through different lenses including presence, interaction, community, participation, collaboration, involvement, and communication. We use the term “involvement” as one of the terms since researchers sometimes broadly used the term engagement to describe their work without further description. Table 6 below provides the description, frequency, and percentages of the various studies related to engagement.

Research sub-themes on engagement.

In the sections below, we provide several examples of the different engagement sub-themes that were studied within the larger engagement theme.

Presence. This sub-theme was the most researched in engagement. With the development of the community of inquiry framework most of the studies in this subtheme examined social presence ( Akcaoglu & Lee, 2016 ; Phirangee & Malec, 2017 ; Wei et al., 2012 ), teaching presence ( Orcutt & Dringus, 2017 ; Preisman, 2014 ; Wisneski et al., 2015 ) and cognitive presence ( Archibald, 2010 ; Olesova et al., 2016 ).

Interaction . This was the second most studied theme under engagement. Researchers examined increasing interpersonal interactions ( Cung et al., 2018 ), learner-learner interactions ( Phirangee, 2016 ; Shackelford & Maxwell, 2012 ; Tawfik et al., 2018 ), peer-peer interaction ( Comer et al., 2014 ), learner-instructor interaction ( Kuo et al., 2014 ), learner-content interaction ( Zimmerman, 2012 ), interaction through peer mentoring ( Ruane & Koku, 2014 ), interaction and community building ( Thormann & Fidalgo, 2014 ), and interaction in discussions ( Ruane & Lee, 2016 ; Tibi, 2018 ).

Community. Researchers examined building community in online courses ( Berry, 2017 ), supporting a sense of community ( Jiang, 2017 ), building an online learning community of practice ( Cho, 2016 ), building an academic community ( Glazer & Wanstreet, 2011 ; Nye, 2015 ; Overbaugh & Nickel, 2011 ), and examining connectedness and rapport in an online community ( Bolliger & Inan, 2012 ; Murphy & Rodríguez-Manzanares, 2012 ; Slagter van Tryon & Bishop, 2012 ).

Participation. Researchers examined engagement through participation in a number of studies. Some of the topics include, participation patterns in online discussion ( Marbouti & Wise, 2016 ; Wise et al., 2012 ), participation in MOOCs ( Ahn et al., 2013 ; Saadatmand & Kumpulainen, 2014 ), features that influence students’ online participation ( Rye & Støkken, 2012 ) and active participation.

Collaboration. Researchers examined engagement through collaborative learning. Specific studies focused on cross-cultural collaboration ( Kumi-Yeboah, 2018 ; Yang et al., 2014 ), how virtual teams collaborate ( Verstegen et al., 2018 ), types of collaboration teams ( Wicks et al., 2015 ), tools for collaboration ( Boling et al., 2014 ), and support for collaboration ( Kopp et al., 2012 ).

Involvement. Researchers examined engaging learners through involvement in various learning activities ( Cundell & Sheepy, 2018 ), student engagement through various measures ( Dixson, 2015 ), how instructors included engagement to involve students in learning ( O'Shea et al., 2015 ), different strategies to engage the learner ( Amador & Mederer, 2013 ), and designed emotionally engaging online environments ( Koseoglu & Doering, 2011 ).

Communication. Researchers examined communication in online learning in studies using social network analysis ( Ergün & Usluel, 2016 ), using informal communication tools such as Facebook for class discussion ( Kent, 2013 ), and using various modes of communication ( Cunningham et al., 2010 ; Rowe, 2016 ). Studies have also focused on both asynchronous and synchronous aspects of communication ( Swaggerty & Broemmel, 2017 ; Yamagata-Lynch, 2014 ).

4.2.2. Research on learner characteristics

The second largest theme was learner characteristics. In this review, we explore this further to identify several aspects of learner characteristics. In this review, we categorized the learner characteristics into self-regulation characteristics, motivational characteristics, academic characteristics, affective characteristics, cognitive characteristics, and demographic characteristics. Table 7 provides the number of studies and percentages examining the various learner characteristics.

Research sub-themes on learner characteristics.

Online learning has elements that are different from the traditional face-to-face classroom and so the characteristics of the online learners are also different. Yukselturk and Top (2013) categorized online learner profile into ten aspects: gender, age, work status, self-efficacy, online readiness, self-regulation, participation in discussion list, participation in chat sessions, satisfaction, and achievement. Their categorization shows that there are differences in online learner characteristics in these aspects when compared to learners in other settings. Some of the other aspects such as participation and achievement as discussed by Yukselturk and Top (2013) are discussed in different research themes in this study. The sections below provide examples of the learner characteristics sub-themes that were studied.

Self-regulation. Several researchers have examined self-regulation in online learning. They found that successful online learners are academically motivated ( Artino & Stephens, 2009 ), have academic self-efficacy ( Cho & Shen, 2013 ), have grit and intention to succeed ( Wang & Baker, 2018 ), have time management and elaboration strategies ( Broadbent, 2017 ), set goals and revisit course content ( Kizilcec et al., 2017 ), and persist ( Glazer & Murphy, 2015 ). Researchers found a positive relationship between learner's self-regulation and interaction ( Delen et al., 2014 ) and self-regulation and communication and collaboration ( Barnard et al., 2009 ).

Motivation. Researchers focused on motivation of online learners including different motivation levels of online learners ( Li & Tsai, 2017 ), what motivated online learners ( Chaiprasurt & Esichaikul, 2013 ), differences in motivation of online learners ( Hartnett et al., 2011 ), and motivation when compared to face to face learners ( Paechter & Maier, 2010 ). Harnett et al. (2011) found that online learner motivation was complex, multifaceted, and sensitive to situational conditions.

Academic. Several researchers have focused on academic aspects for online learner characteristics. Readiness for online learning has been examined as an academic factor by several researchers ( Buzdar et al., 2016 ; Dray et al., 2011 ; Wladis & Samuels, 2016 ; Yu, 2018 ) specifically focusing on creating and validating measures to examine online learner readiness including examining students emotional intelligence as a measure of student readiness for online learning. Researchers have also examined other academic factors such as academic standing ( Bradford & Wyatt, 2010 ), course level factors ( Wladis et al., 2014 ) and academic skills in online courses ( Shea & Bidjerano, 2014 ).

Affective. Anderson and Bourke (2013) describe affective characteristics through which learners express feelings or emotions. Several research studies focused on the affective characteristics of online learners. Learner satisfaction for online learning has been examined by several researchers ( Cole et al., 2014 ; Dziuban et al., 2015 ; Kuo et al., 2013 ; Lee, 2014a ) along with examining student emotions towards online assessment ( Kim et al., 2014 ).

Cognitive. Researchers have also examined cognitive aspects of learner characteristics including meta-cognitive skills, cognitive variables, higher-order thinking, cognitive density, and critical thinking ( Chen & Wu, 2012 ; Lee, 2014b ). Lee (2014b) examined the relationship between cognitive presence density and higher-order thinking skills. Chen and Wu (2012) examined the relationship between cognitive and motivational variables in an online system for secondary physical education.

Demographic. Researchers have examined various demographic factors in online learning. Several researchers have examined gender differences in online learning ( Bayeck et al., 2018 ; Lowes et al., 2016 ; Yukselturk & Bulut, 2009 ), ethnicity, age ( Ke & Kwak, 2013 ), and minority status ( Yeboah & Smith, 2016 ) of online learners.

4.2.3. Less frequently studied research themes

While engagement and learner characteristics were studied the most, other themes were less often studied in the literature and are presented here, according to size, with general descriptions of the types of research examined for each.

Evaluation and Quality Assurance. There were 38 studies (6.14%) published in the theme of evaluation and quality assurance. Some of the studies in this theme focused on course quality standards, using quality matters to evaluate quality, using the CIPP model for evaluation, online learning system evaluation, and course and program evaluations.

Course Technologies. There were 35 studies (5.65%) published in the course technologies theme. Some of the studies examined specific technologies such as Edmodo, YouTube, Web 2.0 tools, wikis, Twitter, WebCT, Screencasts, and Web conferencing systems in the online learning context.

Course Facilitation. There were 34 studies (5.49%) published in the course facilitation theme. Some of the studies in this theme examined facilitation strategies and methods, experiences of online facilitators, and online teaching methods.

Institutional Support. There were 33 studies (5.33%) published in the institutional support theme which included support for both the instructor and learner. Some of the studies on instructor support focused on training new online instructors, mentoring programs for faculty, professional development resources for faculty, online adjunct faculty training, and institutional support for online instructors. Studies on learner support focused on learning resources for online students, cognitive and social support for online learners, and help systems for online learner support.

Learner Outcome. There were 32 studies (5.17%) published in the learner outcome theme. Some of the studies that were examined in this theme focused on online learner enrollment, completion, learner dropout, retention, and learner success.

Course Assessment. There were 30 studies (4.85%) published in the course assessment theme. Some of the studies in the course assessment theme examined online exams, peer assessment and peer feedback, proctoring in online exams, and alternative assessments such as eportfolio.

Access, Culture, Equity, Inclusion, and Ethics. There were 29 studies (4.68%) published in the access, culture, equity, inclusion, and ethics theme. Some of the studies in this theme examined online learning across cultures, multi-cultural effectiveness, multi-access, and cultural diversity in online learning.

Leadership, Policy, and Management. There were 27 studies (4.36%) published in the leadership, policy, and management theme. Some of the studies on leadership, policy, and management focused on online learning leaders, stakeholders, strategies for online learning leadership, resource requirements, university policies for online course policies, governance, course ownership, and faculty incentives for online teaching.

Course Design and Development. There were 27 studies (4.36%) published in the course design and development theme. Some of the studies examined in this theme focused on design elements, design issues, design process, design competencies, design considerations, and instructional design in online courses.

Instructor Characteristics. There were 21 studies (3.39%) published in the instructor characteristics theme. Some of the studies in this theme were on motivation and experiences of online instructors, ability to perform online teaching duties, roles of online instructors, and adjunct versus full-time online instructors.

4.3. Research settings and methodology used in the studies

The research methods used in the studies were classified into quantitative, qualitative, and mixed methods ( Harwell, 2012 , pp. 147–163). The research setting was categorized into higher education, continuing education, K-12, and corporate/military. As shown in Table A in the appendix, the vast majority of the publications used higher education as the research setting ( n  = 509, 67.6%). Table B in the appendix shows that approximately half of the studies adopted the quantitative method ( n  = 324, 43.03%), followed by the qualitative method ( n  = 200, 26.56%). Mixed methods account for the smallest portion ( n  = 95, 12.62%).

Table A shows that the patterns of the four research settings were approximately consistent across the 12 themes except for the theme of Leaner Outcome and Institutional Support. Continuing education had a higher relative frequency in Learner Outcome (0.28) and K-12 had a higher relative frequency in Institutional Support (0.33) compared to the frequencies they had in the total themes (0.09 and 0.08 respectively). Table B in the appendix shows that the distribution of the three methods were not consistent across the 12 themes. While quantitative studies and qualitative studies were roughly evenly distributed in Engagement, they had a large discrepancy in Learner Characteristics. There were 100 quantitative studies; however, only 18 qualitative studies published in the theme of Learner Characteristics.

In summary, around 8% of the articles published in the 12 journals focus on online learning. Online learning publications showed a tendency of increase on the whole in the past decade, albeit fluctuated, with the greatest number occurring in 2018. Among the 12 research themes related to online learning, the themes of Engagement and Learner Characteristics were studied the most and the theme of Instructor Characteristics was studied the least. Most studies were conducted in the higher education setting and approximately half of the studies used the quantitative method. Looking at the 12 themes by setting and method, we found that the patterns of the themes by setting or by method were not consistent across the 12 themes.

The quality of our findings was ensured by scientific and thorough searches and coding consistency. The selection of the 12 journals provides evidence of the representativeness and quality of primary studies. In the coding process, any difficulties and questions were resolved by consultations with the research team at bi-weekly meetings, which ensures the intra-rater and interrater reliability of coding. All these approaches guarantee the transparency and replicability of the process and the quality of our results.

5. Discussion

This review enabled us to identify the online learning research themes examined from 2009 to 2018. In the section below, we review the most studied research themes, engagement and learner characteristics along with implications, limitations, and directions for future research.

5.1. Most studied research themes

Three out of the four systematic reviews informing the design of the present study found that online learner characteristics and online engagement were examined in a high number of studies. In this review, about half of the studies reviewed (50.57%) focused on online learner characteristics or online engagement. This shows the continued importance of these two themes. In the Tallent-Runnels et al.’s (2006) study, the learner characteristics theme was identified as least studied for which they state that researchers are beginning to investigate learner characteristics in the early days of online learning.

One of the differences found in this review is that course design and development was examined in the least number of studies in this review compared to two prior systematic reviews ( Berge & Mrozowski, 2001 ; Zawacki-Richter et al., 2009 ). Zawacki-Richter et al. did not use a keyword search but reviewed all the articles in five different distance education journals. Berge and Mrozowski (2001) included a research theme called design issues to include all aspects of instructional systems design in distance education journals. In our study, in addition to course design and development, we also had focused themes on learner outcomes, course facilitation, course assessment and course evaluation. These are all instructional design focused topics and since we had multiple themes focusing on instructional design topics, the course design and development category might have resulted in fewer studies. There is still a need for more studies to focus on online course design and development.

5.2. Least frequently studied research themes

Three out of the four systematic reviews discussed in the opening of this study found management and organization factors to be least studied. In this review, Leadership, Policy, and Management was studied among 4.36% of the studies and Access, Culture, Equity, Inclusion, and Ethics was studied among 4.68% of the studies in the organizational level. The theme on Equity and accessibility was also found to be the least studied theme in the Berge and Mrozowski (2001) study. In addition, instructor characteristics was the least examined research theme among the twelve themes studied in this review. Only 3.39% of the studies were on instructor characteristics. While there were some studies examining instructor motivation and experiences, instructor ability to teach online, online instructor roles, and adjunct versus full-time online instructors, there is still a need to examine topics focused on instructors and online teaching. This theme was not included in the prior reviews as the focus was more on the learner and the course but not on the instructor. While it is helpful to see research evolving on instructor focused topics, there is still a need for more research on the online instructor.

5.3. Comparing research themes from current study to previous studies

The research themes from this review were compared with research themes from previous systematic reviews, which targeted prior decades. Table 8 shows the comparison.

Comparison of most and least studied online learning research themes from current to previous reviews.

L = Learner, C=Course O=Organization.

5.4. Need for more studies on organizational level themes of online learning

In this review there is a greater concentration of studies focused on Learner domain topics, and reduced attention to broader more encompassing research themes that fall into the Course and Organization domains. There is a need for organizational level topics such as Access, Culture, Equity, Inclusion and Ethics, and Leadership, Policy and Management to be researched on within the context of online learning. Examination of access, culture, equity, inclusion and ethics is very important to support diverse online learners, particularly with the rapid expansion of online learning across all educational levels. This was also least studied based on Berge and Mrozowski (2001) systematic review.

The topics on leadership, policy and management were least studied both in this review and also in the Tallent-Runnels et al. (2006) and Zawacki-Richter et al. (2009) study. Tallent-Runnels categorized institutional and administrative aspects into institutional policies, institutional support, and enrollment effects. While we included support as a separate category, in this study leadership, policy and management were combined. There is still a need for research on leadership of those who manage online learning, policies for online education, and managing online programs. In the Zawacki-Richter et al. (2009) study, only a few studies examined management and organization focused topics. They also found management and organization to be strongly correlated with costs and benefits. In our study, costs and benefits were collectively included as an aspect of management and organization and not as a theme by itself. These studies will provide research-based evidence for online education administrators.

6. Limitations

As with any systematic review, there are limitations to the scope of the review. The search is limited to twelve journals in the field that typically include research on online learning. These manuscripts were identified by searching the Education Research Complete database which focuses on education students, professionals, and policymakers. Other discipline-specific journals as well as dissertations and proceedings were not included due to the volume of articles. Also, the search was performed using five search terms “online learning" OR "online teaching" OR "online program" OR "online course" OR “online education” in title and keyword. If authors did not include these terms, their respective work may have been excluded from this review even if it focused on online learning. While these terms are commonly used in North America, it may not be commonly used in other parts of the world. Additional studies may exist outside this scope.

The search strategy also affected how we presented results and introduced limitations regarding generalization. We identified that only 8% of the articles published in these journals were related to online learning; however, given the use of search terms to identify articles within select journals it was not feasible to identify the total number of research-based articles in the population. Furthermore, our review focused on the topics and general methods of research and did not systematically consider the quality of the published research. Lastly, some journals may have preferences for publishing studies on a particular topic or that use a particular method (e.g., quantitative methods), which introduces possible selection and publication biases which may skew the interpretation of results due to over/under representation. Future studies are recommended to include more journals to minimize the selection bias and obtain a more representative sample.

Certain limitations can be attributed to the coding process. Overall, the coding process for this review worked well for most articles, as each tended to have an individual or dominant focus as described in the abstracts, though several did mention other categories which likely were simultaneously considered to a lesser degree. However, in some cases, a dominant theme was not as apparent and an effort to create mutually exclusive groups for clearer interpretation the coders were occasionally forced to choose between two categories. To facilitate this coding, the full-texts were used to identify a study focus through a consensus seeking discussion among all authors. Likewise, some studies focused on topics that we have associated with a particular domain, but the design of the study may have promoted an aggregated examination or integrated factors from multiple domains (e.g., engagement). Due to our reliance on author descriptions, the impact of construct validity is likely a concern that requires additional exploration. Our final grouping of codes may not have aligned with the original author's description in the abstract. Additionally, coding of broader constructs which disproportionately occur in the Learner domain, such as learner outcomes, learner characteristics, and engagement, likely introduced bias towards these codes when considering studies that involved multiple domains. Additional refinement to explore the intersection of domains within studies is needed.

7. Implications and future research

One of the strengths of this review is the research categories we have identified. We hope these categories will support future researchers and identify areas and levels of need for future research. Overall, there is some agreement on research themes on online learning research among previous reviews and this one, at the same time there are some contradicting findings. We hope the most-researched themes and least-researched themes provide authors a direction on the importance of research and areas of need to focus on.

The leading themes found in this review is online engagement research. However, presentation of this research was inconsistent, and often lacked specificity. This is not unique to online environments, but the nuances of defining engagement in an online environment are unique and therefore need further investigation and clarification. This review points to seven distinct classifications of online engagement. Further research on engagement should indicate which type of engagement is sought. This level of specificity is necessary to establish instruments for measuring engagement and ultimately testing frameworks for classifying engagement and promoting it in online environments. Also, it might be of importance to examine the relationship between these seven sub-themes of engagement.

Additionally, this review highlights growing attention to learner characteristics, which constitutes a shift in focus away from instructional characteristics and course design. Although this is consistent with the focus on engagement, the role of the instructor, and course design with respect to these outcomes remains important. Results of the learner characteristics and engagement research paired with course design will have important ramifications for the use of teaching and learning professionals who support instruction. Additionally, the review also points to a concentration of research in the area of higher education. With an immediate and growing emphasis on online learning in K-12 and corporate settings, there is a critical need for further investigation in these settings.

Lastly, because the present review did not focus on the overall effect of interventions, opportunities exist for dedicated meta-analyses. Particular attention to research on engagement and learner characteristics as well as how these vary by study design and outcomes would be logical additions to the research literature.

8. Conclusion

This systematic review builds upon three previous reviews which tackled the topic of online learning between 1990 and 2010 by extending the timeframe to consider the most recent set of published research. Covering the most recent decade, our review of 619 articles from 12 leading online learning journal points to a more concentrated focus on the learner domain including engagement and learner characteristics, with more limited attention to topics pertaining to the classroom or organizational level. The review highlights an opportunity for the field to clarify terminology concerning online learning research, particularly in the areas of learner outcomes where there is a tendency to classify research more generally (e.g., engagement). Using this sample of published literature, we provide a possible taxonomy for categorizing this research using subcategories. The field could benefit from a broader conversation about how these categories can shape a comprehensive framework for online learning research. Such efforts will enable the field to effectively prioritize research aims over time and synthesize effects.

Credit author statement

Florence Martin: Conceptualization; Writing - original draft, Writing - review & editing Preparation, Supervision, Project administration. Ting Sun: Methodology, Formal analysis, Writing - original draft, Writing - review & editing. Carl Westine: Methodology, Formal analysis, Writing - original draft, Writing - review & editing, Supervision

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

1 Includes articles that are cited in this manuscript and also included in the systematic review. The entire list of 619 articles used in the systematic review can be obtained by emailing the authors.*

Appendix B Supplementary data to this article can be found online at https://doi.org/10.1016/j.compedu.2020.104009 .

Appendix A. 

Research Themes by the Settings in the Online Learning Publications

Research Themes by the Methodology in the Online Learning Publications

Appendix B. Supplementary data

The following are the Supplementary data to this article:

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Review article, applying best practice online learning, teaching, and support to intensive online environments: an integrative review.

• 1 Monash Online-Psychology Education Division (MO-PED), Faculty of Medicine, Nursing and Health Sciences, School of Psychological Sciences, Monash University, Clayton, VIC, Australia
• 2 Melbourne Centre for the Study of Higher Education, The University of Melbourne, Melbourne, VIC, Australia
• 3 Faculty of Medicine, Nursing and Health Sciences, School of Psychological Sciences, Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Clayton, VIC, Australia

Demand for flexible online offerings has continued to increase as prospective students seek to upskill, re-train, and undertake further study. Education institutions are moving to intensive modes of online study delivered in 6- to 8-week study periods which offer more frequent intake periods. Prior literature has established key success factors for non-intensive (12–13 weeks) online offerings; for teachers, skill development is critical to promote a flexible, responsive approach and maintain technological capabilities; for students, an ability to navigate the technology, interact with the learning environment in meaningful ways, and self-regulate learning is important, as the absence of physical infrastructure and opportunities for face-to-face interactions in online environments places a greater emphasis on alternate forms of communication and support. The current paper explores known best practice principles for online instructors, students, and student support and considers how these might apply to intensive online environments. It is suggested that the accelerated nature of learning in intensive settings may place additional demands on students, instructors, and support mechanisms. Further research is imperative to determine predictors of success in online intensive learning environments.

The scope and availability of online offerings continues to expand globally. Demand for more intensive, short-term courses that provide opportunities for up-skilling has increased in the wake of massive open online courses (MOOCs), and this increased demand has in turn expanded the availability of online degree programs. As many as six million students in the USA were undertaking online education in 2015, with nearly five million of these students studying an undergraduate college (tertiary) qualification ( Allen and Seaman, 2017 ). Similar trends have been noted in the Australian context. Recent scoping reports of the Australian Higher Education sector have highlighted continual, rapid growth in online enrollments, but also a degree of “blurring” of boundaries, due to the increased adoption of technologies to support the on-campus learning experience ( Norton and Cherastidtham, 2014 ; Norton and Cakitaki, 2016 ). Changes to Australian funding policy have also enabled more public universities to invest in online offerings ( Kemp and Norton, 2014 ), contributing to the continuing growth of this sector.

Online modes of study have been found to be equivalent to on-campus environments with respect to key outcomes such as student academic performance ( Magagula and Ngwenya, 2004 ; McPhee and Söderström, 2012 ) and student satisfaction ( Palmer, 2012 ). However, online offerings also pose some key differences to on-campus modes of study. Accessing course materials online allows unprecedented levels of flexibility and accessibility for students from around the world and overcomes geographical barriers that might prevent students accessing on-campus course offerings ( Brown, 1997 , 2011 ; Bates, 2005 ). The nature of the online education environment also means that course delivery needs to compensate for the lack of immediate physical infrastructure, relying more heavily on asynchronous methods of communication. There is also emerging evidence that online student cohorts differ from on-campus cohorts with respect to factors such as age and work or family commitments ( Bailey et al., 2014 ; Johnson, 2015 ), which also speaks to the demand for more flexible, career-driven online offerings. The requirements of online students as a distinct demographic are another factor for consideration when planning and developing an online course. Furthermore, from a course development perspective, there is increasing understanding that developing online courses is more complex than merely translating written materials to an online format; it requires careful planning and maximization of available online technologies to cater for a variety of individual differences, student timetables and external commitments, and assessment modes (e.g., Rovai, 2003 ; Grant and Thornton, 2007 ; Rovai and Downey, 2010 ). Online learning does not only differ for students but also carries implications for instructors. Online instruction places varying demands on delivery and feedback methods and relies on different teacher knowledge and skills than face-to-face tuition ( Alvarez et al., 2009 ). It is evident that a sensitive approach catering to both similarities and differences of both modes of study is warranted.

With the abovementioned differences between on-campus and online education in mind, there is a duty for online education providers to continue to research and implement best practice for online modes of study. As fully online offerings continue to develop, new modes of delivery necessitate continual adjustment and evaluation to ensure that courses meet student needs. One such development is the move toward intensive mode courses. Intensive online degree courses (hereafter referred to as “intensive online courses”) are those in which students complete a degree entirely online, within an accelerated timeframe compared to the typical on-campus learning experience. Units of study are also delivered in shorter timeframes than the traditional (in an Australian context) 12- or 13-week semester, sometimes comprising 6 or 8 weeks of intensive learning, where a similar amount of material is covered compared with a semester structure. Students typically complete one unit at a time (as compared to four units concurrently for a traditional on-campus semester). Intensive online degree programs have built on the success of MOOCs to help upskill, and in some cases provide certified professional development, over a faster timeframe than typical on-campus university courses ( Laurillard, 2016 ). MOOCs aside, the literature base on intensive online learning for degree programs in particular remains limited. With the potential for tertiary institutions to move more toward this mode of offering, which provides for increased student intake to meet growth demands, there is a need to more comprehensively evaluate the factors that contribute to student and instructor success in an intensive online learning environment. The present integrative review aims to bring together acknowledged best practices in online education, with a view to considering how these may apply in an intensive online education environment. In particular, the elements that comprise a successful online experience for instructors and students, and the provision of student support and well-being services are considered.

Online Teaching: Critical Factors

As online modes of study continue to expand, there is increasing awareness of the need for competent online instructors. Developing institutional competence for online instruction requires a careful approach to training online instructors and a workload investment in staff training and development ( Gregory and Lodge, 2015 ). While it is acknowledged that face-to-face teaching competencies such as knowledge of curricula and pedagogy do transfer to online contexts, it is also important to recognize the unique competencies required for online teaching success, and the role of institutions in setting instructor duties and responsibilities ( Alvarez et al., 2009 ). Despite much prior research attention exploring the notion of online student readiness, online instructor readiness is now emerging as an equally important construct ( Oomen-Early and Murphy, 2009 ).

There is consensus in prior literature that effective online instruction requires a more flexible approach to skill development, due to the variety of roles and skills applied in online contexts ( Bawane and Spector, 2009 ). Key environmental differences between online and on-campus learning environments also necessitate the development of different online teaching competencies. A sample of existing frameworks for teacher competencies in online education is summarized in Table 1 below.

Table 1 . Established teacher competency frameworks in online education.

The ability to effectively communicate, manage technology, and deliver and assess content becomes especially important in intensive online environments, where there is less available time to acclimatize to new tools and operating environments. The monitoring of student progress, identification, and follow-up of issues or barriers are also critical duties for instructors to minimize the likelihood of student disengagement or withdrawal.

Online learning systems employ a variety of online tools, systems, and software, which place new demands on the technical competence of instructors ( Volery and Lord, 2000 ). Modes of communication also differ in online courses, with a greater reliance on asynchronous communication methods ( Hung et al., 2010 ). Live, “virtual” classrooms may also involve remote but instant methods of feedback between student and instructor, facilitated through live chat, video/webcam interactions, and small-group “break-out rooms.” The development of student rapport also differs in online contexts, and the nature of how rapport is initiated and maintained in online settings is not always easily comparable to face-to-face teaching. Naturally, assessment and feedback are also delivered in different ways via asynchronous methods when teaching online. Clear assessment practices, including communication of deadlines and assessment requirements, have been found to positively influence student engagement and course completion ( Thistoll and Yates, 2016 ).

Institutional and research-based efforts to characterize the competencies required for effective online instruction (e.g., Goodyear et al., 2001 ; Dennis et al., 2004 ; Darabi et al., 2006 ; International Board of Standards for Training, Performance and Instruction, http://ibstpi.org/ , as cited in Beaudoin, 2015 ) suggest a degree of overlap in the conceptualization of the core teacher competencies required for effective online instruction. Some of the most important online teacher competencies drawn from the aforementioned studies include:

• communication skills;

• technological competence;

• provision of informative feedback;

• administrative skills;

• responsiveness;

• monitoring learning;

• providing student support.

Without adequate technological skills, instructors risk being unable to resolve technology-related problems during live class, which may impact student access to learning materials. Communication skills are also paramount ( Easton, 2003 ). Effective instructor–student communication in online learning environments relies on timely and clear interactions through a variety of formats ( Easton, 2003 ), including email, chat, live class questions, and assessment and feedback provision. In the absence of more immediate feedback methods available to on-campus instructors (e.g., face-to-face consultation), the assessment and feedback provided in online learning environments needs to be as clear and valuable as possible to promote student understanding ( Darabi et al., 2006 ). Teacher support online involves effective monitoring of student progress, anticipation and resolution of key learning queries, and establishment and maintenance of rapport. Collectively, these kinds of competencies shape the effectiveness of online instructors and, in turn, the student experience. While these elements are well established as effective practice in online tuition, there exists significantly more pressure on these factors when content delivery, assessment, feedback, and communication occur within a condensed 6- to 8-week timeframe.

In addition to student-related benefits, there is evidence that online instructor training can provide benefits to instructors themselves ( Roblyer et al., 2009 ). These benefits occur both through expansion of direct skills for the instructor (i.e., professional development) to build confidence in online environments, and also through skills that are transferable to on-campus contexts ( Roblyer et al., 2009 ), providing a wider institutional benefit. Roblyer et al. (2009) note a kind of “reverse impact phenomenon” whereby teachers have experienced transferred skills improvements in face-to-face tuition by enhancing online teaching skills. While these authors based the outcomes around K-12 teachers, it is likely that the gains experienced by teachers (e.g., improved self-reflection on teaching and assessment methods; increased sensitivity toward student needs) would be similarly relevant to on-campus tertiary teachers. It is also important, however, to consider the environmental challenges posed by more intensive teaching timeframes. Instructors delivering content in shorter blocks of time have less time to reflect on, adapt and amend content before the next unit delivery, and thus unit re-design and content development can be more of a challenge in intensive online environments.

Effective online instructors have a direct and important role in influencing the student experience, since instructors are often the “face” of an online course. Prior studies have emphasized instructor presence as among the most critical of factors related to student success online ( Easton, 2003 ; Menchaca and Bekele, 2008 ; Kennette and Redd, 2015 ; Kim and Thayne, 2015 ). In the absence of the richness of interactions available to on-campus students, instructors become an even more important “ingredient” in helping to engage, retain, and graduate online students. Instructors also play a key role in motivating students throughout their online study ( Bolliger and Martindale, 2004 ), since instructors may commonly be the only personalized point of contact provided to students at any one time. Instructor responsiveness and availability has been highlighted as a key predictor of online student satisfaction, in that lack of timely feedback or slow communication timeframes from instructors detract from student satisfaction online ( Bolliger and Martindale, 2004 ). It is apparent that development of instructor training is a critical component of effective institutional preparation for wholly online courses, so that teachers can develop the range of skills required to teach online successfully.

When considering the applicability of teacher competencies to an intensive online environment, it is reasonable to assume that the faster-paced nature of intensive learning may require greater competence with respect to certain instructor skills. The building of teacher competencies is a process that requires institutional planning and reflection when considering a move to more intensive online degree offerings, so that instructors are supported to flourish and students can benefit from quality instruction. The Technological Pedagogical Content Knowledge (TPACK) model proposed by Mishra and Koehler (2006) (see Figure 1 below) provides a useful framework through which to view teacher competencies across multiple levels, and we can apply this model to consider teacher skills in intensive online environments.

Figure 1 . The Technological Pedagogical Content Knowledge (TPACK) model ( Mishra and Koehler, 2006 ). Reproduced by permission of the publisher, © 2012 by tpack.org .

The TPACK model promotes meaningful integration of technology, content knowledge, and pedagogy ( Mishra and Koehler, 2006 ). Thus, an instructor’s ability to utilize technology as the basis for timely, responsive and clear feedback becomes even more critical in an intensive online environment, which can be further exacerbated by a lack of time to resolve technical issues or system access problems. Since technology is inherently embedded in content delivery and influential in approaches to teaching, technical competence must also be highlighted alongside content proficiency and pedagogical knowledge for instructors seeking to teach online, especially in intensive environments. It is apparent that the demands on all of these skill areas are likely to be heightened in an intensive delivery setting, and further research to understand the nature of any additional skill demands in intensive online environments would be valuable.

Online Learning: Critical Factors

Effective approaches to online education must also take account of baseline learner competencies and characteristics. Demographically, there are consistent differences between on-campus and online students ( Bailey et al., 2014 ). For instance, more women than men appear to choose online modes of study ( Price, 2006 ). Further, online learners are typically older than on-campus students, with many being “mature-age” students between the ages of 25 and 50 ( Moore and Kearskey, 2005 ). This also presents a rich opportunity to enhance the learning environment through incorporation of some of the life experiences of older learners online ( Boston and Ice, 2011 ; O’Shea et al., 2015 ). Greenland and Moore (2014) also noted the potential for unexpected work commitments and/or busy work schedules to contribute to student intermissions and discontinuations.

With regard to factors that influence student choice to study online, there is evidence that students opting to study online choose flexibility (i.e., convenience) over the perceived value of studying on-campus ( Bolliger and Martindale, 2004 ). This flexibility is likely to be prioritized due to many online students being at a later life stage than younger on-campus students, whereby study must be accommodated around work and family commitments. However, the source of a requirement for flexibility also brings with it additional complications: factors such as age, gender, educational history, work obligations, and family commitments have all been found, in turn, to impact on completion rates in tertiary education settings ( Tsay et al., 2000 ; Colorado and Eberle, 2010 ).

Becoming an online learner places different demands on students. The fundamental quality and nature of the student experience shifts in online learning environments to a greater reliance on asynchronous modes of communication. Interactions also occur through a variety of methods, including learner-to-content, learner-to-instructor, and learner-to-learner (peer) interaction ( Bolliger and Martindale, 2004 ). This necessitates a more proactive, self-directed approach on the part of students ( Brown, 1997 ; Tsay et al., 2000 ; Khiat, 2015 ; Kırmızı, 2015 ). Self-regulated learning, where students use meta-cognitive skills to plan, implement, and reflect on their learning, have been increasingly associated with better academic achievements ( Johnson, 2015 ; Khiat, 2015 ). Active engagement in academic materials, and with instructors and peers, has been emphasized as a core component of successful learning for students ( Pascarella and Terenzini, 2005 ). In one study, lack of social interaction was found to be the largest single barrier to student success online ( Muilenburg and Berge, 2005 ). Meaningful connections with the institution are a key ingredient in student engagement ( Pascarella and Terenzini, 2005 ).

However, not all of the responsibility for effective engagement in online courses lies with the student. There is an institutional and faculty responsibility to create an inclusive, supportive structure where students can engage in social interactions and a sense of (online) community can be fostered, as has been apparent in research findings from Garrison and colleagues in applying and extending the Community of Inquiry model (e.g., Garrison et al., 2000 ; Aragon, 2003 ; Garrison and Cleveland-Innes, 2005 ; Garrison and Arbaugh, 2007 ) (see Figure 2 below).

Figure 2 . Community of Inquiry model ( Garrison et al., 2000 ). Reproduced with permission from the author.

This sense of belonging is a key component that impacts student engagement and can act as a buffer against attrition ( Oomen-Early and Murphy, 2009 ). As well as understanding and promoting the factors that can enhance belonging in an online community, faculty also have a responsibility to monitor student progress and address any early signs of difficulty or disengagement ( Beaudoin, 2002 ; Dennen, 2008 ).

A number of factors and situations can act as barriers to effect student engagement in online study, and online environments have long been known to face higher attrition rates than on-campus modes of study ( Oomen-Early and Murphy, 2009 ). Many of these elements stem from the unique challenges and opportunities of online learning environments discussed above:

• technical difficulties;

• perceived isolation;

• challenges balancing study;

• work and family commitments;

• confusion with content;

• poor academic performance; or

• lack of motivation.

Thus, understanding how best to gauge student readiness or preparedness for online study is a critical institutional responsibility. A range of recent studies have sought to characterize the main factors underlying readiness for online study ( Vonderwell, 2004 ; Watkins et al., 2004 ; Pillay et al., 2007 ; Mercado, 2008 ; Dray et al., 2011 ; Farid, 2014 ; Wladis et al., 2016 ). Collectively, these studies emphasize the importance of technical skills, effective time management, individual differences (especially self-directed or self-regulated learning), financial means, and online self-efficacy as elements of readiness. A range of measures have also been developed and validated to assess student readiness for online learning ( Kerr et al., 2006 ; Mercado, 2008 ; Hung et al., 2010 ; Dray et al., 2011 ), but there is scope in future research to consider the notion of student readiness more directly, as it relates to readiness for intensive online learning. In this mode, one could argue that there is an increased responsibility for faculty to screen students on commencement, to pre-empt and remedy potential barriers to a successful online study experience. Further, a more holistic approach to defining student readiness that encompasses key psychological, technological, situation, and learning-related contributors to readiness for intensive online study is recommended.

Intensive online courses are likely to involve many of the same benefits and challenges for students as non-intensive courses. However, it is of note that the faster pace of the learning environment inherent in intensive courses means that both students and instructors have less time to address any key concerns, provide remedial support, or rectify any unintended technical or learning delays. Thus, the process of monitoring student progress and potential barriers is paramount in intensive online learning environments.

Online Environment: Student Support and Well-Being Services

Consideration of student support services becomes paramount in intensive online environments, where disruptions to technology or lack of support services can pose a significant barrier to student engagement in learning. Students completing courses wholly online are often limited in their access to the entire variety of support services a university offers, compared to their on-campus counterparts ( Lee, 2010 ). The “four pillars” of supporting student success (see Figure 3 below) are often the intangibles that educators might take for granted when providing fully online courses. These pillars include online-friendly academic supports ( Coonin et al., 2011 ; Huwiler, 2015 ), assistance with navigating technology ( Lee, 2010 ), health and well-being facilities ( Anderson, 2008 ), and a sense of belongingness, or community ( Kumar and Heathcock, 2014 ).

Figure 3 . The “four pillars” to supporting student success.

Ensuring a positive and rewarding experience for online students, particularly those enrolled within intensive online courses, is contingent upon the institutional provider offering equitable support structures that are also appropriately translated into the online environment ( Pullan, 2011 ). Being already prone to higher attrition rates, fully online students adopting study via intensive modes have increased expectations of their instructors, and the course learning environment more broadly, to provide the necessary infrastructure required to manage the increased workload. Therefore, tertiary providers choosing to deliver fully online courses, particularly intensive courses, need to ensure that these four pillars are prioritized equivalently to the translation of content into online platforms in order to maximize student success and reduce risks for attrition.

The first pillar, and arguably the most crucial support an institution can offer to online students, revolves around online-friendly academic resources and ample opportunities for student–instructor interaction ( Cannady, 2015 ). The success of completing a tertiary degree online strongly depends on the student’s ability to work autonomously and manage their time effectively ( Wang et al., 2013 ). Beyond the personal qualities students must possess to succeed in an online course, as previously discussed, there is also a growing need for the institution offering the course to provide appropriate online-friendly academic scaffolding that supports their students throughout their learning ( Lee and Choi, 2011 ). This includes, but is not limited to, detailed orientation services, and comprehensive library resources.

Providing orientation services, especially for online students, is essential in order to adequately integrate incoming cohorts into their new online learning environment ( Cho, 2012 ). Research, albeit limited, has consistently shown that orientation programs have improved student retention and academic performance both on- and off-campus ( House and Kuchynka, 1997 ; Williford et al., 2001 ; Wilson, 2005 ). When looking specifically at online courses, the factors that contribute to a successful orientation include comprehensive overviews of the course structure, recommended time commitments and expectation of students, familiarization with required instructional media and software, and guidance on the communication tools needed for student–staff interactions. Delivering this information in an online environment requires a substantial rethink of the way in which these programs are designed ( Smyth and Lodge, 2012 ). Despite the challenges, providing these resources before a student commences their course has been shown to be critical for reducing early drop-out rates, increasing self-confidence, and enhancing the students’ sense of belonging ( Tomei et al., 2009 ). However, many institutions that offer online courses do not make their orientation program mandatory before commencement, while some choose not to deliver an orientation program at all. In fact, one study has suggested up to 29% of institutions only offer on-campus orientation programs, despite also offering fully online courses ( Cannady, 2015 ), perhaps due to the difficulty in developing effective online orientation. This rate is particularly concerning given there is strong evidence to show that comprehensive orientation programs are vital to supporting online student success.

Comprehensive, course-specific resources created to improve students’ academic performance are also pivotal to student success, and are best delivered when strong collaborations between online instructional staff and the institution’s librarians are prioritized ( Arnold et al., 2002 ; Kumar and Heathcock, 2014 ). Many university libraries provide an abundance of resources that assist new students transitioning into tertiary life ( Arnold et al., 2002 ). However, if online course providers are unaware of the technological and/or literacy competencies of their students, these library resources may not be properly disseminated to incoming cohorts. This is problematic for fully online courses, particularly those offered in intensive modes where demands are greater, if the only exposure to their institution required is via their course’s learning management system (LMS). Targeted training programs and easy access to comprehensive resources available online is therefore vital to improving student success in intensive online learning environments; simply providing generic resources via a course’s LMS without proper instruction may not be sufficient to meet online student needs ( Kumar and Heathcock, 2014 ). It is important that instructors gauge their student’s competencies before commencing the course so that any necessary gaps, particularly those easily fulfilled with existing library resources, can be addressed appropriately.

The second pillar, yet one of the most immediate and unique hurdles for online students, is the need to provide adequate technical scaffolding in order to prepare students for learning in an online-only environment ( Shea et al., 2005 ). Tertiary institutions offering fully online courses need to assure that all technology requirements are clearly communicated to students before commencing the course, and that ongoing technical support is provided to reduce delay in meeting course expectations. This is particularly important for intensive modes of online study where assessment deadlines leave little to no room for technical-based hurdles. The strong relationship between a student’s acceptance of technology and their perceived satisfaction with online courses is also important to consider, as this may pose additional hurdles to incoming cohorts unaccustomed to learning in an online environment ( Lee, 2010 ). As emphasized earlier in this review, where students or instructors lack the required technical competence, this can pose a significant and sometimes insurmountable barrier, contributing to student discontinuation or disengagement from the course. Thus, adopting a user-friendly learning environment and flexible online technical support is critical for intensive online courses in order to increase student retention and engagement.

Beyond the need to overcome technological obstacles are the pressures of academic achievement, transitioning to university life and time management; all which benefit from the third pillar that is health and well-being support. These factors create increasing stress among students, both on- and offline ( Robotham and Julian, 2006 ). University student cohorts have been found to have concerning rates of mental health issues ( Andrews and Wilding, 2004 ; Bayram and Bilgel, 2008 ; Hjeltnes et al., 2015 ), and online student cohorts, particularly those adjusting to intensive study modes, face comparable challenges. In response, several efforts have been made by universities to support students and promote positive mental health and well-being in an attempt to combat increasing psychological distress ( Regehr et al., 2013 ). One example is the effort to extend support programs to online students which are already available to on-campus students, such as personal counseling and career services ( Dare et al., 2005 ; Lapadula, 2010 ). However, this solution often does not account for the many online students who are not in the required geographical district needed to access these services, in person or via phone.

One potential solution to the geographical hurdle is for institutions to invest in online counseling or self-help services, to reach beyond their usual audience who utilize traditional face-to-face services ( Tokatlidis et al., 2011 ). This option holds promise as a means of creating services with sufficient flexibility to allow access for students from a diverse range of locations. Another wide-reaching strategy demonstrating increasing efficacy among university students is mindfulness. In recent years, mindfulness—the practice of bringing attention to the present moment, non-judgementally—has substantially grown in popularity, particularly within education contexts where research has shown that mindfulness can benefit students experiencing high rates of psychological distress ( Cavanagh et al., 2013 ). The efficacy of mindfulness-based practices within primary and secondary schools ( Zenner et al., 2014 ), as well as at tertiary level ( Regehr et al., 2013 ), has been well documented and shows promising results in improving resilience against common student-related stressors. The benefits of advancing technology has also seen an increasing number of online mindfulness programs rolled out, which have positive implications for the growing popularity of fully online tertiary courses ( Sable, 2010 ). Yet despite this, the benefits of integrating online mindfulness-based practices into completely online courses is scarcely researched. The need for evidence-based interventions and prevention strategies is especially crucial given that literature suggests around 50% of university students experience significant levels of psychological distress while enrolled ( Regehr et al., 2013 ). Provision of psychological services is made more difficult for online students who may not otherwise have access to any other form of mental health support ( Lapadula, 2010 ). Therefore, more research is required into appropriate prevention and intervention strategies for high rates of distress among students involved in intensive online learning, given the added pressures they face with shorter course deadlines and being physically segregated from their peers.

The last pillar required to support student success comes with prioritizing a sense of belongingness and community to any fully online cohort. Fostering open dialog between students, instructors, and their fellow classmates is essential to online learning which can often be taken for granted during the implementation of online courses ( Coomey and Stephenson, 2001 ). As alluded to earlier in this review, online students require personalized, timely feedback on assessments ( Li and Beverly, 2008 ; Lee, 2010 ), equivalent community-like interactions with peers via social networking platforms such as Facebook and Twitter ( Roblyer et al., 2010 ; Akcaoglu and Bowman, 2016 ; Tang and Hew, 2017 ), and ideally 24-h academic and technical support services ( Lorenzo and Moore, 2002 ) in order to succeed in online learning. In particular, research has identified that adequate quality and quantity of interaction between a student and their instructor is associated with increased student course satisfaction ( Lee, 2010 ; Ralston-Berg et al., 2015 ). Therefore, it is necessary for institutions to prioritize offering effective means for communication within the online learning environment, not bound by physical or geographical segregation. For example, one study has suggested that the use of asynchronous activities, such as introducing yourself via video posts and conducting online discussion forums, may be useful in combatting the issues of isolation and lack of a “sense of community” commonly found among online students ( Trespalacios and Rand, 2015 ). Given the shorter timeframe required for students to meet course deadlines via intensive modes, it becomes critical that students feel continuously supported, and that this support is fostered by the infrastructure of their online learning environment. Further research has also suggested that there are benefits to including students and instructors’ input into the development and implementation of online courses, which can assist in keeping students engaged and thus achieve success ( Roby et al., 2013 ). Each of these pillars, particularly when equally prioritized in fully online course delivery, ultimately best equip students to succeed in their course from orientation through to graduation.

Summary: Applications to Intensive Online Learning Environments

In reflecting on the discussion points raised in the current review, it is apparent that online environments and intensive online environments are likely to share many “ingredients” in common. Both contexts share similar modes of communication, structures, learning materials and methods, assessment principles, and skills requirements of both instructors and students. Nevertheless, the compressed timeframes involved in intensive online learning mean that the reliance on effective communication, technology, learning, and feedback strategies increases, and the corresponding demands on teacher and learner competencies are higher.

Instructor presence remains a critical factor in all modes of online study, and particularly so in intensive online environments, where instructors need to work to establish and maintain student engagement. Pedagogical approaches need to account for learner competencies, characteristics, and preferred learning approaches. This is especially important given the emerging demographic differences between online and on-campus cohorts. Intensive online learning environments should take account of potential barriers that can lead to increased attrition, such as perceived isolation, competing work/family commitments, poor motivation, lack of engagement with content, and technical challenges. There are particular time pressures evident in an intensive online course when needing to identify and rectify such barriers, and regular monitoring of student progress can help to quickly identify and address potential concerns. Providing comprehensive orientation services is key to ensure students are adequately informed and linked to ongoing support services. Communication plays a pivotal role in enhancing the online learning experience through peer-to-peer and student-to-instructor dialog. Ongoing flexible technical support is also vital to manage any technical issues that arise. Finally, well-being services and the provision of online well-being content such as mindfulness resources are important steps toward the prevention of online student mental health concerns.

On a more general note, a flexible and responsive approach to all activities is critical in intensive online environments. Where there are student or instructor skills gaps, it becomes more time-critical to identify and address these, or potential barriers can become a greater risk of student attrition. Likewise, if students are not able to adopt a proactive approach to time management and prioritize study deadlines, the risk of overwhelm and stress increases. Academically, understanding key content and successfully completing assessment tasks becomes of greater importance in the intensive online environment. Future research would benefit from understanding any specific factors related to student and instructor readiness for intensive online study, so that institutions adopting intensive study modes can provide the maximum chance of a successful experience for all involved.

It is apparent that intensive online courses offer a range of benefits to students and staff, including accessibility, opportunities for embracing new technologies, and promoting independent, self-regulated learning. These benefits need to be considered alongside some of the known barriers associated with online education; potential student disengagement, work-life balance difficulties for students working full-time, and technological challenges for both students and instructors. It is imperative to continue to monitor and meet student needs that are particular to the online environment, so that online courses can adapt to changing future needs. With the move for tertiary institutions to consider more intensive modes of online degree study comes an increased responsibility to understand how best to prepare students, instructors, and student support mechanisms to succeed in intensive online learning environments. Consideration of the factors discussed in the current review will guide institutions and educators to maximize student success in intensive online courses as this sector continues to rapidly evolve. Future research is well positioned to continue deepening understanding of best practice as it applies to intensive online education.

Author Contributions

CR wrote and refined the introductory, instructor- and student-focused, and conclusion sections of the review. DA wrote and refined the component relating to student support and well-being. All team members were involved in publication planning, reading of drafts and suggestions for changes, and feedback on the final publication draft. SM, MM, and JL were additionally involved in providing strategic advice on directions for the paper, and the role of the paper within the research team agenda.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors wish to thank Professor Kim Cornish and Associate Professor Matthew Mundy for supporting the creation of the Monash Online – Psychology Education Division (MO-PED) team and associated research outputs. The authors also wish to acknowledge funding support supplied via the Monash Pearson Alliance. The team also wishes to thank Leah Braganza and Tony Mowbray for their time in providing feedback on the draft manuscript.

Completion of the current review was funded by the School of Psychological Sciences, Faculty of Medicine Nursing and Health Sciences, Monash University.

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Keywords: online education, intensive online learning, student experience, teacher education, higher education

Citation: Roddy C, Amiet DL, Chung J, Holt C, Shaw L, McKenzie S, Garivaldis F, Lodge JM and Mundy ME (2017) Applying Best Practice Online Learning, Teaching, and Support to Intensive Online Environments: An Integrative Review. Front. Educ. 2:59. doi: 10.3389/feduc.2017.00059

Received: 16 August 2017; Accepted: 26 October 2017; Published: 21 November 2017

Reviewed by:

Copyright: © 2017 Roddy, Amiet, Chung, Holt, Shaw, McKenzie, Garivaldis, Lodge and Mundy. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Chantal Roddy, chantal.roddy@monash.edu

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Please note you do not have access to teaching notes, online education in schools.

International Journal of Educational Management

ISSN : 0951-354X

Article publication date: 1 December 2000

Online education with Internet technology has been used extensively in post‐secondary education, but it is relatively new in schools. It has considerable potential for enhancing teaching/learning in both traditional schools and home‐schooling. Describes research conducted in Alberta where online education in schools is more common than in any other Canadian province. Semi‐structured interviews were held with 13 teachers from four online schools. Although they reported increased workloads and stress associated with added responsibilities for authoring online courses, providing technological support, and enhancing their technological skills, the teachers perceived many benefits of online education. However, improvements in the scope and reliability of technology and better access to digital educational content are required to realize the full potential of online education in schools. The information presented has relevance to school systems in many countries. It also relates to the rapidly evolving role of technology in education for all ages.

• Online computing
• Distance learning

Muirhead, W.D. (2000), "Online education in schools", International Journal of Educational Management , Vol. 14 No. 7, pp. 315-324. https://doi.org/10.1108/09513540010378969

Copyright © 2000, MCB UP Limited

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Exploring the direct and indirect effects of EFL learners’ online motivational self-system on their online language learning acceptance: the new roles of current L2 self and digital self-authenticity

• Amir Reza Rahimi   ORCID: orcid.org/0000-0003-4963-3442 1 &
• Zahra Mosalli   ORCID: orcid.org/0000-0001-6175-2976 2  

Asian-Pacific Journal of Second and Foreign Language Education volume  9 , Article number:  49 ( 2024 ) Cite this article

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The impact of students' intrinsic or extrinsic motivations on their future intentions for online language schooling has been widely documented, but further emphasis needs to be placed on examining motivation beyond traditional theories. Thus, the current study sought to pivot the focus from intrinsic and extrinsic motivation to university language learners’ L2 self-identities in shaping their intention to learn language online. Toward this, we extended the technology acceptance model by integrating language learners’ L2 motivational self-system (L2MSS). Accordingly, 422 Iranian territory students who learned language online completed surveys covering language motivation and attitudes toward online language learning. The results of partial least squares structural equation modeling validated that current L2-self and digital self-authenticity can be used as separable subcomponents of L2MSS in the Iranian territory context. Moreover, learners with a higher level of future self-image and current L2 self-description found online learning more useful and easy to use. A further finding revealed an authenticity gap among higher educators since they were more motivated to learn language online than in face-to-face classrooms. Besides introducing a new conceptual framework into the literature, the researchers suggest that as a way of influencing higher education language learners’ intentions towards online language learning, lecturers should uncover language learners’ future ideal selves in advance of attending this online language course and design their language syllabus accordingly. It is also imperative for instructors to encourage students to self-describe their progress during online courses as it influenced their behavioral intention to learn languages online.

Introduction

With globalization, the notion of “deterritorialization” of language has emerged (Blommaert, 2010 , p. 46) meaning that English does not belong to native speakers; instead, it is a lingua franca which everyone can learn and acquire. However, due to the diversity of the learning contexts, language learners may rely on psychological factors such as motivation, attitudes, and self-regulation (Farid & Lamb, 2020 ; Rahimi & Cheraghi, 2022 ) to preserve and succeed in their language learning process. Among these psychological factors, motivation has been known as the critical detriment and prerequisite factor that language learners need in order to acquire a second language (L2; Dörnyei & Ryan, 2015 ). As such, Dörnyei ( 2009 ) conceptualized traditional motivation, which does not depend on learners' pre-learning objectives, such as intrinsic, instrumental, and integrative motivations (Gardner, 1985 ). Infact, He holds that language learners' motivation is not determined by their interaction with the target language context but rather by their successful engagement with the actual usage of the language, and their self images (Farid & Lamb, 2020 ). Indeed, motivation is grounded on recognizing a disparity between current language skills and those sought in the future, and language learners strive to fill this void (Dörnyei, 2009 ).

Presently, information and communications technologies (ICTs) are successfully integrated into language classrooms and bring more educational opportunities than traditional language instruction. However, the successful implication of any ICTs in language classrooms relies, to a large extent, on language users' attitudes towards them (Hsu, 2022 ; Rahimi, 2023 ). Having been highlighted by recent studies, positive attitudes towards ICTs would lead users to accept the target ICTs (Hsu, 2022 ; Rahimi, 2023 ; Wang et al., 2022 ). Align with this, some contemporary researchs have emphasized a need to explore users' attitudes towards ICTs through psychological (Chen et al., 2020 ; Hsu & Lin, 2021 ), professional (Rahimi & Tafazoli, 2022 ), and technological factors (Hsu, 2022 ), helping administrators to have a better understanding of users’ continued intention to design their programs. With specific attention to the psychological aspects, scholars posited that motivation is one of the main psychological factors acting as a prerequisite for developing language learners' psychological development (Fathali & Okada, 2018 ; Rahimi & Cheraghi, 2022 ; Zheng et al., 2018 ) and is the critical determinant for language learners to continue their effort (Dörnyei & Ryan, 2015 ; Lamb, 2017 ), and attitude (Fathali & Okada, 2018 ; Hsu, 2022 ; Hsu & Lin, 2021 ) in OLL. Confirming these views, recent studies in language education and educational technology have explored the role of college-age students’ extrinsic and intrinsic motivation (Hsu & Lin, 2021 ), integrative and instrumental motivation (Dai et al., 2022 ), self-efficacy (Rosli & Saleh, 2022 ), self-determination theory (SDT, Fathali & Okada, 2018 ; Hsu, 2022 ) or flow theory (Wang et al., 2022 ) on shaping their attitudes toward online language learning or mobile-assisted language learning (MALL). The question raised here concerns the role of territory students' process-oriented motivation, such as the L2 Motivational Self-System (L2MSS), in influencing their behavioural intention regarding online language learning in the future.

Thus, in the current study, we attempted to cover the current gap and explore the relationship between learners' L2MSS and their attitudes towards online language learning. The first purpose of this study was to validate the factorial structure of the L2MSS in the Iranian higher education context. A second objective is to determine the direct, and indirect impact of college students' L2-selves identities on their intention to learn languages online. In order to accomplish theses objects, the scholars formulated the following research questions:

What are the factorial structures of the Iranian EFL learners’ online motivational self-system, including ideal L2-self, ought-to L2-self, current L2-self, digital self-authenticity, and self-attribution in online language learning?

To what extent do Iranian EFL learners’ ideal L2-self, ought-to L2-self, current L2-self, digital self-authenticity, and self-attribution directly predict their perceived ease of use and usefulness and indirectly predict their behavioural intention to learn a language online?

Literature review

The l2 motivational self-system.

In the field of language psychology, Dörnyei’s ( 2009 ) L2MSS replaced one of the most conventional L2 motivation models, scilicet integrativeness. By synthesizing Markus and Nurius’ ( 1986 ) Possible Selves Theory and Higgins’ ( 1987 ) Self-Discrepancy Theory, Dörnyei L2MSS encompasses three dimensions: ideal L2 self, the ought-to L2 self, and L2 learning experience (Dörnyei, 2005). He declared that the initial attitude towards learning a language is the “successful engagement with the actual language learning process” (Dörnyei, 2009 , p. 29) rather than integrating with target community. Thus, the ideal L2 self alludes to the language learners’ future self-image to learn another language and to reach their ideal image, and objects in the future. The second aspect of L2MSS accounts for attribution that language learners should pass in order to avoid adverse outcomes, such as learning English for passing their language course. The language learning experience are intended to represent learner-situated and executive motivations in relation to the immediate learning context such as teaching materials, and teachers themselves all of which impact language learners’ motivation (Fig.  1 ).

Study hypothesized model

The L2MSS has been utilized by various studies and known as one of the most predicting frameworks to uncover language learners’ attitudes towards learning another language (Farid & Lamb, 2020 ). Al-Hoorie's ( 2018 ) in his meta-analysis of L2MSS reported that, over 32 studies applied this theoretical framework to 30,000 cases across various language contexts, showing the predictive power of this theorithical framework in exploring language learners’ motivation. It was also highlighted by Yousefi and Mahmoodi ( 2022 ) that this theoretical framework impacted language learners' motivation toward learning since 17 published studies involving 18,832 participants applied this framework. Therefore, they indicated that the L2MSS provides a solid theoretical framework for examining motivational behaviors among language learners. Although it appears to be an effective tool for learners in different language learning contexts, target languages, and learning outcomes, its construction is multifaceted, and its effectiveness varies with learners’ age, gender, educational level, and geographical location, showing its context-specific nature, which is required to determine in Iranian EFL context, higher education, and online language learning context. Particulalrly, its new constructs, such as digital self-authenticity and the current L2-self, needs to be validate, and explore language learners’ motivational bahavior in various language learning context (Henry & Cliffordson's, 2017 ; Rahimi, 2023 ; Smith et al., 2020 ; Thorsen et al., 2017 ). Based on these considerations, and recoemndetations we used L2MSS as the theoretical framework and operationalized the its components based on Henry and Cliffordson's ( 2017 ) and Smith et al.'s ( 2020 ) conceptual models as follows:

Ideal L2-self (IDL) University language learners’ personal future images of what they want to be by learning a language online, such as becoming a native speaker in the future.

Ought to L2-self (OUL) College language learners acknowledge’ to learn English online to pass some rubric such as passing their general English course in university to go next semester.

Digital self-authenticity (DSA) University learners’ dedicating motivation in online language class comparison with their face-to-face university class.

Self-attribution (SA) College students attribute their success or failures in online language learning to their effort, motivation, and attitude.

Current L2-self (CU) University students’ self-descriptions about their current competence and abilities to use and learn language online, which has a reciprocal relationship with language contexts such as teachers, social expectations, and peer pressure.

Technology acceptance model (TAM)

The TAM model proposed by Davis ( 1989 ) was based on the theory of reason action (TRA; Fishbein & Ajzen, 1980 ), examining human behavior. However, TAM is employed, tested, and extended to dig further into any computer-based behavior in different learning contexts (Rahimi, 2023 ). More specifically, Davis ( 1989 ) introduced four underlying variables: perceived usefulness (PU), perceived ease of use (PEU), attitude (AT), and behavioral intention (BI). PU alludes to the user's beliefs that the target system will improve his job performance (Davis, 1989 ). PEU deals with the degree to which a user supposes that implementing a particular system would be free of physical and mental effort (Davis, 1989 ). Moreover, in this model, AT refers to users' feelings about the behavior and performing it (Fishbein & Ajzen, 1977 ). Finally, BI presents the learner's intention to employ the target system in the future.

TAM delves into the relationships among these four variables. In accordance with this model, PU can be predicted by PEU. Simply put, when learners assume that the learning process can be efficient through a specific system, they will perceive a positive attitude towards it. Moreover, learners' belief in the easiness of system use leads to a positive direct effect on PU and ATT. Additionally, it indirectly affects BI, in which ATT is a mediator effect.

Current study: research model and hypotheses development

There have been calls for discovering the discrepancy between language learners’ ideal L2 self and current L2 self in different language learning contexts (Henry & Cliffordson, 2017 ; Kangasvieri & Leontjev, 2021 ; Smith et al., 2020 ), digital self- authenticity (Rahimi, 2023 ; Smith et al., 2020 ) in OLL, extending previous findings in applied linguistics, and psycholinguistics (Rahimi & Cheraghi, 2022 ; Smith et al., 2020 ; Wang et al., 2023 ), exploring the role of language learners’ motivation, particularly L2 selves on their language learning behaviors in OLL (Rahimi, 2023 ; Rahimi & Cheraghi, 2022 ; Zheng et al., 2018 ), extending the TAM model with other variables and escalating its explanatory powert in other contexts (Granić, 2022 ; Granić & Marangunić, 2019 ), and discovering the role of motivation in shaping language learners’ technology acceptance in Computer-Assisted-Language Learning (CALL; Hsu, 2022 ; Hsu & Lin, 2021 ; Rahimi, 2023 ; Wang et al., 2022 ). We heed these calls by expanding the works by Henry and Cliffordson ( 2017 ) and Smith et al. ( 2020 ) and extending the TAM model with university students’ L2 selves in an online language learning context.

As mentioned earlier, researchers have proposed that ideal L2 self, ought-to L2 self, and L2 learning experience (Dörnyei & Ryan, 2015 ) serve as the basis for language motivation, particularly in OLL (Rahimi, 2023 ; Smith et al., 2020 ). Based on previous studies college students’ ideal L2 self is positively correlated with their psychological behaviors, such as self-regulation, learning performance, and effort in online language context (Rahimi & Cheraghi, 2022 ; Smith et al., 2020 ; Zheng et al., 2018 ). Conversely, the ought to L2-self was negatively correlated with university learners’ language learning performance and behaviors (Shen et al., 2020 ). Hsu and Lin ( 2021 ) further demonstrated that college students' intrinsic motivation had a positive relationship with their PEU and PU toward MALL, while their extrinsic motivation had a negative association. In Iranian higher education, the impact of these two constructs on language learners' attitudes towards OLL still needs to be clarified. Accordingly, we hypothesized that Iranian university students with a greater sense of their future self will be more positive about the usefulness and ease of online language learning, whereas students with passing rubrics are likely to perceive online language learning as less useful and easier to use.

H1: Ideal L2 self positively predicts ease of the use (H1a), and usefulness (H1b) online language learning.

H2: Ought-to L2 self negatively predicts ease of the use (H2a), and usefulness (H2b) online language learning.

Online language learning has received a wide currency among territory language learners due to its flexibility and availability outside of the learning context (Rahimi & Tafazoli, 2022 ). There is evidence that uninstructed context negatively influenced learners’ motivation (Lamb & Arisandy, 2019 ) and effort (Henry, 2013 ). These findings were triggers for Henry ( 2013 ) to address the “authenticity gap” that students will experience “suppressed self-authenticity” and ultimately “lower motivation and satisfaction” (as cited in Smith et al., 2020 , p. 3). Ushioda ( 2011 ), however, argued that the rigid context of the classroom environment hindered language learners' self-expression, creativity, and motivation. Moreover, Henry and Cliffordson ( 2017 ) found that the informal out-of-classroom context negatively correlated with learners’ learning efforts and weakened their motivation in tarditional classes. By replicating their work in the Chinese context, Smith et al. ( 2020 ) found that both digital self-authenticity and general authenticity positively predicted Chinese university learners’ language efforts. A deeper understanding of the authenticity gap in Iranian EFL context, especially in territory education, needs to be gained. In light of this gap, we develop the following hypotheses:

H3: Digital self-authenticity positively predicts ease of the use (H3a), and usefulness (H3b) online language learning.

Addressing the need for more exploration of learners’ motivation, researchers have begun to investigate the key motivational cognitions, including learners’ beliefs and attribution (Smith et al., 2020 ). According to Weiner ( 2010 ), attribution alludes to a particular notion that learner has about their success or failure, relating to their effort to complete target activities. Markus and Ruvolo ( 1989 ) mentioned that individual beliefs constitute their possible selves. In other words, beliefs root in consciousness and have a reciprocal correlation with individual possible selves and motivation (Dörnyei, 2009 ). According to Smith et al. ( 2020 ), attribution were linked with L2MSS and effort. What is more needed is the role of learners’ attribution in shaping their attitudes towards OLL. To explore this gap we hypothesized that:

H4: Self-attribution positively predicts ease of the use (H4a), and usefulness (H4b) online language learning.

In addition to the L2MSS constructs, the current L2 self is another construct that has recently been added and validated to online language motivation by Smith et al. ( 2020 ). According to Kangasvieri and Leontjev ( 2021 ), it represents learners’ self-descriptions about their current competence and abilities to use language which has a reciprocal relationship with language contexts such as teachers, social expectations, and peer pressure. Previous surveys have emphasized the role of current L2-self in shaping territory learners’ learning performance (Henry & Cliffordson, 2017 ; Smith et al., 2020 ; Thorsen et al., 2017 ), in spite of this fact, its emotional impact on language learners' has not been clearly identified, the researchers proposed the following hypotheses:

H5: Current L2 self positively predicts positively predicts ease of the use (H5a), and usefulness (H5b) online language learning.

According to Davis ( 1989 ), perceived usefulness is predicated on perceived ease of use, and both of them significantly would predict users’ attitudes towards the system. As declared by Hair et al. ( 2021 ) moderating variables will render more generalizability of the research findings. The two antecedents of attitudes in the TAM model, however, were neglected in some studies, (i.e., Şahin et al., 2022 ; Tseng et al., 2022 ) leading to a misperception that their mediating role in shaping users' intentions towards the target system. Our study will identify them as mediators between learners' online L2 self-identities and attitudes toward online language learning. Consequently, we hypothesized that both PEU and PU would a mediator role between college learners’ L2MSS and their attributions within their attitudes. That is to say, should online learning be easy to use, students will find it helpful, resulting in a positive attitude toward online learning. Moreover, attitudes can mediate university students’ PEU and PU and their intention to use online learning in the future.

H6: Learners’ perceived ease of use of online learning positively predicts perceived the usefulness (H6a), and attitudes toward (H6b) online language learning.

H7: Learners’ perceived usefulness positively predicts attitudes towards online language learning.

H8: Learners’ attitudes toward online language learning positively predict their behavioral intention.

Drawing on two theoretical frameworks and previous findings on L2MSS and TAM, this study proposed a model in which L2MSS variables, self-attribution, and digital self-authenticity were the predictors of PEU and PU and both positively mediated learners’ attitudes towards online language learning, and behavioural intentions. However, ought to L2-self will have a negative correlation with the two indicators. Moreover, three antecedents of behavioural intention, including perceived usefulness, perceived ease of use, and attitudes, mediate the relationship between language learners’ online motivational self-system and their behavioural intention to learn a language online in the future.

Methodology

Research context, and participants.

The study was conducted at two universities in Ardabil between the winter and summer semesters of 2020–2022. Prior to the collection of the data, the ethics committee of the university and related departments approved the data collection. During each semester, two general English classes were held three times a week, each lasting 4 months. Participants had access to a learning management system (LMS) hosted by Mohaghegh Ardebili and the Azad University of Ardabil. Their four skills of writing, speaking, reading, and listening were covered in the general course content. All participants received the same course materials and were taught by the same professor (second author). Two criteria were required for students to pass their course semester: evaluation of their speaking skills as an oral exam and their online language test as their final written exam covering the course. As a result of the requirement for a large number of responses to validate the study constructs and to cover learners' L2 self-identities, the researcher has chosen a simple non-probability sampling method because all participants are equally and independently eligible for the study (Ary et al., 2006 ). In fact, it covers populations with various backgrounds, specialists, and educational experiences who might learn the language to pass their ought-to L2 self, learn it for personal self-image (Ideal L2-self), or assess their current competence and abilities in language learning (Current L2 self). Thus, the random sampling method is consistent with the study objective since it enhances generalizability, allows for comprehensive coverage of all factors, and helps researchers to minimize biasing the results.

A pool of 422 participants took part in this study, of whom 227 were female (53.8%), and 195 were male (46.2%) to took part in general English class, majoring in law (N = 109), mechanical engineering (N = 119), science (N = 104), and psychology (N = 90). With respect to age, all participants were within the age range of 18, and above 27. Table 1 shows the participants’ democraphic information.

Instruments

The data was collected through the questionnaire which was of three parts in this study. The first part tapped into the participants’ demographic information, namely gender, age, major, and informed constent. The second part had 18 items rated on a 7-point Likert type scale in which 1 represented strongly disagree to 7 represented strongly agree . This part deals with the learners’ motivational self-system in an online language learning context. We measured learners’ IDL with four items such as “e.g., If my dreams come true, I will use English effectively in the future” (Henry & Cliffordson, 2017 ). The Out to L2-self with three items such as “I have to learn English online because I don’t want to fail in general English course” Were adapted from Zheng et al. ( 2018 ). The digital self-authenticity four items including “ I get greater personal motivation when I speak English online than I do when I speak face-to-face classrooms” Were adapted from Smith et al. ( 2020 ). Similarly, three self-attribution items such as “My skills in using English in online class are largely come from my own natural ability” were adapted from Henry and Cliffordson ( 2017 ). The current L2-self with four items such as “I see myself as someone who is good at speaking/using English in online environment” were adapted form Smith et al. ( 2020 ).

The third part of the questionnaire measured participants’ attitudes towards online language learning with 15 statements. Four items of PEU, including “It is easy for me to learn English online” Were adapted from Davis ( 1989 ). Moreover, four items of PU such as “online language learningenables me to pass my general English course in university” were adapted from Davis ( 1989 ). The behavioral intention (BI) and attitude with three and four items were adapted from Atif et al. ( 2015 ). A sample item of AT includes, “Online language learning is a good idea for gerneral English class in university,” and BI includes “I intend to use online learning cources next semester for my generallanguage class in university”.

Data analysis procedure

The statistical package for the social sciences (SPSS) and PLS-SEM were applied for data analysis in this study. The multivariate analysis was executed by grafting principal components analysis with ordinary least squares regressions composite-based model (Hair et al., 2021 ). The primary reasons for choosing this method in the current study were four reasons: (1) the composite-based models were not sensitive to the normality of datasets (Hair et al., 2021 ) and it has a casual-predictive nature and is suitable for explanatory research and theory development (Ringle et al., 2012 ). Additionally, the PLS-SEM analyses the data simultaneously in two complex phases, namely the measurement and structural phases. During the first phase, indicators correlation to latent variables are specified, and in the second phase, relationships between latent variables are examined (Hair et al., 2021 ; Sarstedt et al., 2014 ). Additionally, it could evaluate both moderation and mediation effects simultaneously (Sarstedt et al., 2020 ). Compared with covariance-based models, composite-based models are more effective at predicting complex models and correlations (Hair et al., 2021 ).

Measurement model

It is necessary to calculate the descriptive statistics of variables before we move on to the measurement model. As per Table  2 , the average means of all variables were greater than 3.5, indicating that most students opted for the top options.

The measurement model sought to estimate the relationship between latent variables and their indicators in four steps compromising: (1) evaluating the factor loading, (2) assessing the reliability, (3) assessing the convergent validity, and (4) assessing the discrminant validity (Hair et al., 2021 ).

To measure reliability, Cronbach's alpha and composite reliability (rho_a) and (rho_c) were employed. Hair et al. ( 2021 ) recommend a cut value of 0.70 for all of them. According to Hair et al. ( 2021 ), convergent and discriminant validity should be considered as part of the PLS-SEM measurement phase. The convergent validity shows the degree to which a latent construct explains the variance of its indicators (Hair et al., 2021 ). In this line we used the average variance extracted (AVE) to test convergent validity. It is equivalent to the commonality of a group of indicators and is calculated by summing all squared loadings of a set of indicators (Hair et al., 2014 ). AVE is equivalent to the commonality of a group of indicators and is calculated by summing all squared loadings of a set of indicators (Hair et al., 2014 ). Hair et al. ( 2021 ) recommend that AVE should be above 0.50. As shown in Table  2 , all latent variables had reliability and validity values above 7.0 and 5.0.

The researchers simultaneously applied the Fornell–Larcker criterion ( 1981 ) as well as the Heterotrait-Monotrait ratio (HTMT) criteria proposed by Henseler et al. ( 2015 ) when assessing discriminant validity, which is a measure of whether a construct is empirically distinct from the other constructs in the structural model simply put, how well it measures what it is intended to measure (Hair et al., 2021 ).

According to Fornell and Larker ( 1981 ) the values of √A should be higher than the other constructs and their relationship with other constructs, or in other words variances between the construct and its indicators are greater than those between any other construct. or Each construct's AVE must be greater than its highest squared correlation with any other construct in order to satisfy this requirement. Table  3 displays latent variables’ discriminant validity.

Morover, The HTMT assesses the mean value of indicator items among the other indicators' mean of average correlation. According to Henseler et al. ( 2015 ), this mean value should be less than 0.85 or 0.90. Table 4 displays the HTMT results.

Structural model

Using the higher-order structural model, the model's predictive power was estimated to uncover latent variable causal correlations. For this sake, Path coefficient (β) was applied to estimate the significance of the hypothetical causal correlations between the latent variables. As addressed by Hair et al. ( 2021 ), β value should range from − 1 to + 1; the higher the level of value, the higher the significance of the relationship among the variables. The f-square was also implemented to measure the effect size and the intensity correlation among the latent variables. The values of 0.02, 0.15, and 0.35 showing small, medium, and large effect sizes, respectively (Hair et al., 2021 ).

Moreover, the variance inflation factor (VIF) is applied to evaluate the level of collinearity. According to Hair et al. ( 2021 ), a multicollinearity problem occurs when the VIF of all endogenous variables is over 4.0, showing no collinearity problem between the model's independent variables. Table 5 illustrates that all the paths in the model were statistically confirmed; the coefficient correlations within HEs' L2 motivational self-system with PEU and PU support H1a, H1b, H3a, H3b, H4a, H4b, H5a, H5b, However, the OUL had positive and significant coefficients with both PU and PE, thus the H2a, and H2b were rejected. The H6a, H6b, H7, and H8 were significant and supported study hypotheses. Furthermore, the intensity of the effect of CU on PEU was greater than that of other online self-motivational variables. PU also exerted a more significant effect on AT than PEU. A hypothesis is confirmed or rejected if the t-value exceeds 1.96 and the p-value is less than 0.05. Table 5 shows each path's path coefficient, effect size, and significant levels.

A direct analysis was performed with a subsample of 5000 to determine the direct effects of OLLM on attitudes and behavioural intention constructs. All motivational factors were directly associated with learners' attitudes and their behavioural intentions, with digital self-authenticity being the most strongly associated (DSA → AT; β = 0.124 Coefficient of Interval (CI); 0.074–0.177). The results of the direct analysis are shown in Table  6 .

In order to explore the role of mediator variables on the direction between language learners' online motivational, their attitudes, and behavioral intentions, the indirect bootstrap analysis was applied. The result of the serial mediation analysis showed that except (IDL → PEU → PU → AT → BI; \(\upbeta =0.010\) ), (OUL → PEU → PU → AT; \(\upbeta =0.010\) ), (OUL → PEU → PU; \(\upbeta =0.022\) ), (DSA → PEU → PU → AT; \(\upbeta =0.011\) ), (CU → PEU → PU → AT → BI; \(\upbeta =0.012\) ;), (OUL → PEU → PU → AT → BI; \(\upbeta =0.07\) ), (DSA → PEU → PU → AT → BI; \(\upbeta =0.08\) ), and (SA → PEU → PU → AT → BI; \(\upbeta =0.009\) ), the three antecedents to language learners' behavioral intentions significantly mediated the relationship between language learners' online motivation, and their future intention to learn language online. Table 7 displays the result of the serial mediation analysis.

A coefficient of determination ( R 2 ), was used to measure the robustness of exogenous variables in predicting endogenous latent variables. (Hair et al., 2021 ). It shows the proportions of variation for exogenous variables (L2 selves) and grafted effects on endogenous variables (attitudes). The values of 0.19, 0.37, and 0.067 indicate weak, moderate, for this criteria (Hair et al., 2021 ). The coefficient of determination of BI (0.489), AT (0.345), PEU (0.493), and PU (0.490) have been obtained at a suitable level, and changed 48.9% of BI, 34.5% of AT, 49.3% of PEU, and 48% of PU. Moreover, the predictive relevance ( Q 2 ) was applied to the model, and all of them were above zero (Hair et al., 2021 ). As Table  8 demonstrates, all the model's endogenous variables had a suitable level, suggesting the appropriate predictive power of the model.

Lastly, the model fit was estimated by goodness of fit (GOF) and standardized root mean square residual (SRMR). As mentioned by Hu and Bentler ( 1999 ), good SRMR values are less than 0.10 and 0.08. Similarly, the values of 0.01, 0.25, and 0.36 are weak, medium, and general solid fit GOF (Wetzels et al., 2009 ). The GOF and SRMR were calculated to be 0.626 and 0.043, respectively, which indicates a strong model fit. The structural model fit indices are presented in Table  8 . Figure  2 result of structural model.

Result of structural model

The factorial structure of online L2 motivational self-system

The study's initial objective was to validate the factorial structure of the present conceptual model at the territory level of the Iranian EFL context, especially the recent additions of digital-self authenticity and current L2-self. As a result, the reflective phase of the PLS-SEM validated these L2 motivational. Thus, in addition to validating the Current L2-self in Finland (Kangasvieri & Leontjev, 2021 ) and Chinese (Smith et al., 2020 ) and Digital self-authenticity in Chinese (Smith et al., 2020 ), Sweden (Henry & Cliffordson, 2017 ), and LMOOC (Rahimi, 2023 ) contexts, the first phase of the study validated these two L2MSS components in Iran, for higher education in an online language context, and added it to the language motivation literature.

Relationship between online motivational self-system, and language learners’ attitudes

To gain insight into the relationship between learners' motivation and their attitudes, the formative model showed that language learners’ ideal L2-self predicted both PU (β = 203), and PEU (β = 213). Accordingly language learners who had positive future images for learing language find online language learning to be a convenient and useful instrument to reach their vivid personal images. This echoes the previous studies which discovered that learners with intrinsic motivation had a positive attitude towards ICTs integration in language learning (Hsu & Lin, 2021 ). The possible ground for this result might rest on Iranian EFL learners' relatively higher level of ideal L2 self particularly in the online language context (Rahimi, 2023 ). According to Rahimi ( 2023 ) Iranian EFL learners with higher levels of instrumentality-promotion (e.g., being able to be a fluent speaker) and positive attitudes towards English language culture (e.g., watching TV programs online) took online courses effectively. Thus, they perceived online courses as an effective tool in which they could reach their future goals and contact with the target culture without effort.

Aside from confirming that language learners' ideal L2-self influences effort (Smith et al., 2020 ) and online self-regulation (Rahimi & Chearghi, 2022 ; Wang et al., 2023 ), this finding lends additional evidence to the literature regarding its role in shaping language learners' online attitudes. Moreover, the casual correlation between the ideal L2 self and PEU was more potent than PU, meaning that college language learners perceived the ease of using OLL more than its usefulness as a tool that will help them reach their future image of learning the language. This finding contradicts Hsu and Lin ( 2021 ), who found that intrinsic motivation had a more substantial effect on language learners' PU than PEU in Mobile Assisted Language Learning (MALL).

Unexpectedly, OUL had positive correlation with two antecedents; however it had a minor variance in predicting PEU ( β  = 0.152) and PU ( β  = 0.123) among the ideal selves' variables. Due to this, the participants who learn the language to pass some rubrics or merely satisfy others perceived the process of online language learning to be more valuable than its actual use. It is important to note that these findings contrast with those reported in previous international studies, indicating that OUL negatively correlated with Chinese college students' psychological behavior, such as online self-regulation (Zheng et al., 2018 ) and their learning performance (Shen et al., 2020 ; Wei & Xu, 2021 ) since we found a positive correlation between OUL and emotional behavior among Iranian university students. A plausible explanation for the results of the current study may be that university students must pass their online general English course to continue their education; therefore, they may learn English to pass certain rubrics to meet their educational objectives. According to Rahimi and Cheraghi ( 2022 ), Iranian EFL learners with a high level of instrumentality-prevention and others’ expectation can positively manage their online language learning by selecting their time, setting goals, asking for help, and evaluating themselves in an online language learning context.

To our knowledge, this study is the first study tended to examine the authenticity gap in the Iranian higher education, and OLL. Accordingly, the DSA positively predicted university learners' perceived ease of use and usefulness of online language learning. Thus, the sign of authenticity gap in the Iranian higher education was discovered. This finding, in line with Henry and Cliffordson ( 2017 ), posited that the authenticity gap, mainly digital, negatively influenced learners' efforts (particularly in western countries). According to Henry and Cliffordson ( 2017 ), "experiences of frustrated authenticity" (p. 20) negatively influence language learning behaviors as they dedicate more time and effort to the digital learning context than the traditional one. It also could be related to the navigability, adaptability, and multimodality aspects of OLL, as language learners access an ample number of authentic and virtual materials, and they do not need to follow teachers’ mirrors in-class tasks,  repeat what the teachers pronounce, or adhere to a particular set of grammar rules (Rahimi, 2023 ). It can be concluded that, in the Iranian territory education context, learners have higher motivation to take an online course in comparison with face-to-face university classes as a means to improve their English beyond their structured context (Hsu, 2022 ) to reach their ideal future self (Smith et al., 2020 ), to integrate with target culture (Wang et al., 2023 ; Zheng et al., 2018 ), to immigrate to English countries (Rahimi, 2023 ), or to solve their problem with peers in digital games (Ushioda, 2011 ). This result contradicts Lamb ( 2017 ) and Smith et al. ( 2020 ) who reported no authenticity gaps in Indonesian and Chines EFL contexts.

As Smith et al. ( 2020 ) stated the intention might encourage Chinese EFL learners to be self-authentic to keep up with global English. This study identified a correlation between students' intentions and their digital authenticity which was recommended by Smith et al. ( 2020 ) to be examined. Thus, Iranian college students found OLL as a self-authentic context that helped them acquire English with a lower level of effort and find it a powerful instrument for language learning. It seems possible that these results are due to the participants since they had a higher level of ideal L2 self (see hypotheses 1 and 2) towards OLL, leading them to perceive online contexts as useful instruments to dedicate more motivation and attitude towards it since they communicate in forums, and virtual worlds (Lamb & Arisandy, 2019 ; Ushioda, 2011 ). Another possible explanation for this is that in our online language class, the second author cover “birding activities” (Thorne & Reinhardt, 2013 ) she integrated both traditional language schooling and learners' life experience and future needs, bringing about learners' high engagement and agency out of class.

The outer model also revealed that learners' attribution positively predicted both ease of use ( β  = 0.192) and usefulness ( β  = 0.155) of online language learning. Based on this result, it can be inferred that Iranian university learners attribute their success in OLL to its ease of use and usefulness and their locus shifted to online language learning. If learners perceive a higher sense of success in an online language setting, they follow a higher level of attitudes towards it, which is driven by the ease of use and usefulness of target context. This result contradicts Smith et al. ( 2020 ) declared that Chinese university learners attribute their language success to a structured learning context. Deci and Ryan ( 1985 ) support our assertion in their SDT theory which posited that learners are intrinsically motivated to non-instructional language settings as they can learn anytime, anywhere. Similarly, Henry and Cliffordson ( 2017 ) found that language learners were likely to attribute their success to unstructured learning contexts rather than school-related activities.

The formative analysis also showed that learners' current L2 self had the highest shared variance among the L2 selves latent variables as this construct could predict online learning as ease of use ( β  = 0.257), and usefulness ( β  = 0.205). This finding is exhilarating, though, not expected. This implied that learners with current language competence and skills could learn English without effort to achieve their objectives. This is in line with keep on chasing , addressed by Thorsen et al. ( 2017 ), that language learners always want to attain better by evaluating their current language skills and future goals. Hence, it can be concluded that university learners with the current language competence and their future ideal image recognize online language learning as a tool to reach their goals with less effort.

The correlation between college students’ current L2 selves and the perceived usefulness of online language learning was also notable. This result might be due to Iranian EFL learners' high level of self-evaluation in online language learning. According to Rahimi and Cheraghi ( 2022 ), Iranian EFL learners with a higher level of L2 selfs evaluated themselves positively in the online language learning course, causing them to rethink and improve the level of their current fit as the course progressed. Another possible explanation is the role of peers’ and others' expectations in shaping language learners' current L2 self (Kangasvieri & Leontjev, 2021 ; Yung, 2019 ), impacting their attitudes towards online language learning.

Recent studies reported contradictory discoveries about the correlation between perceived ease of use, perceived usefulness, and users' attitudes. In some surveys, perceived ease of use positively contributed to both perceived usefulness and users' attitudes (Fathali & Okada, 2018 ; Hsu & Lin, 2021 ), while other studies claimed that it did not have any significant influence (Wang & Wang, 2009 ). Our finding is consistent with the first group of studies. Furthermore, the higher the level of participants’ attitudes, the more their willingness to enroll in online language schooling. Accordingly, the result suggested that Iranian university learners perceived less difficulty in learning English online which, in turn, contribute to their perceptions about the system possibilities for learning English online and shaping positive attitudes towards its performance and willingness to utilize online language learning in the future. These results may be explained by the fact that online language learning provides flexible and ubiquity features with a user-friendly context that language learners could commute with peers, evaluate their current performance (current L2 self), ask for help from others to pass their criteria (out to L2-self), and reach their future self that might relate to their attribute for success (ideal L2 self and self-attribution).

Coming to indirect analysis, the bootstrap analysis showed that all the online motivational self-system constructs were significantly associated with language learners' attitudes and behavioral intentions toward online language learning. Further, a discrepancy was found between Current L2-self and ought L2-self, in predicting language learners' attitudes toward online language learning, which was recently reported in shaping Chinese online language learning effort (Smith et al., 2020 ). Furthermore, the indirect bootstrap analysis revealed that except nine serial correlations, including (IDL → PEU → PU → AT → BI) (OUL → PEU → PU → AT) (DSA → PEU → PU → AT) (SA → PEU → PU → AT), (DSA → PEU → PU → AT), (IDL → PEU → PU → AT), (CU → PEU → PU → AT → BI) (CU → PEU → PU → AT), (DSA → PEU → PU → AT → BI) and (SA → PEU → PU → AT → BI), all the serial correlations were significantly supported. Thus, in all these non significance serial correlations, the mediators acted as moderators rather than mediators because they indirectly altered significant directions into nonsignificant ones (MacKinnon, 2011 ). As a result, these variables caused the flipping effect; accordingly, the direct-only non-mediation observed as the direct effect was significant, while the indirect one was nonsignificant (Hair et al., 2021 ). Furthermore, a supperiaon also occurred by PU and AT since they escalated the variance shared variance from 0.82 in (SA → BI) to 93 in (SA → PU → AT → BI).

In light of exploring the role of motivation in language learning, the present study delved into validating the new factorial structure of L2MSS in online language learning in the Iranian EFL context and uncovering these factors shaping language learners' attitudes toward OLL. In general, participants perceived the ease of the use of OLL more than its uses, meaning that the easy-to-use features of OLL help language learners to achieve their future ideal image, pass others' expectations, develop their own current L2-self, and attribute their success in learning language in such a context. Since our model predicted 49% of language learners' behavioral intentions to learn a language online, the study has implications for pedagogy and practice in our field.

Theoretical contribution

In five points, the study findings had theoretical contributions to the Applied linguistic, psycholinguistic, CALL, and language motivation. Firtsly, the study validated that the current L2, and ideal L2-self were distinct from each other in Iranian EFL context, and OLL. Seccondly, the study is the first study that tended to cover the authenticity gap in the territory level and validated digital self-authenticity as a separable sub-component of L2MSS in higher education. These discrepancies are conceptualized as a critical determinant of college students’ intention toward online learning context. Another noteworthy contribution is extending previous studies on the role of language learners' motivation and L2 self-image in shaping language learners' learning behaviors, such as their effort (Henry & Cliffordson, 2017 ; Smith et al., 2020 ), self-regulation (Rahimi, 2023 ; Wang et al., 2023 ; Zheng et al., 2018 ) to their attitudes in online language learning. Furthermore, this study introduced further extension model of TAM to CALL, recommended by previous researchers (Granić, 2022 ; Nkomo et al., 2021 ) by shedding more light on language learners’ L2MSS. Moreover, the current survey shifted the view from discovering the role of motivation (e.g., extrinsic, intrinsic, or goal orientation) in shaping language learner technology acceptance to CALL, and higher education (Fathali & Okada., 2018 ; Hsu, 2022 ; Hsu & Lin, 2021 ; Wang et al., 2022 ) to illuminate their motivational self-system such as current L2 self, self-attribution, and digital self-authenticity that have not been addressed in the previous literature. At last, the research introduced another conceptual model for L2MSS and TAM, focusing on territory education, opening doors for future researchers to shift their perspectives from traditional motivational theories to process-oriented views.

Practical implication

Taking a macro, meso, and micro perspective, the findings have practical implications. At the first level, higher educators should run a need analysis, uncover language learners’ ideal future selves to learn a language online and provide their online language course syllabi based on them. Not only should lecturers design their language course based on their learners’ future needs, but they should also cover the rubrics and others’ expectations that language learners should pass to achieve their goals in online language learning. They should also escalate language learners’ current L2 self by providing a competitive atmosphere in their online university classes as learners not only compete with others but also compete with their current self and acquire L2 self-enhancing techniques. Secondly, by encouraging learners to evaluate themselves and increase their expectations of success based on their current abilities, they will develop positive self-images as language users.

At the second level, pedagogical experts should run some teacher training courses for university instructors and make them aware of them to design their syllabus and online language teaching procedure based on language learners’ ideal L2 self image and Ought to self-identities for learning the language. In addition, they should instruct university instructors to encourage their language learners to self-assess themselves as the course progresses to facilitate the development of the learners' current L2 self.

A more important point is that learners had a higher level of attitudes and intentions toward learning a foreign language online and perceived this context as more authentic than traditional language classes. Executive managers should invest more money and infrastructure into integrating online language training into as many classrooms as possible so that it can be utilized as a blended, flipped, or virtual exchange rather than offered as an option in an emergency situation.

At the third level, the study develops a new conceptual model that language scholars should validate, extend, or revise in other EFL or ESL contexts.

This exploratory study revealed the systematic relation between university learners’ L2 motivational selves and their attitudes toward online language learning. To our knowledge, this research is just the starting point for discovering college students’ L2MSS within their attitudes toward online language learning in higher education. Moreover, it is the first attempt to explore the authenticity gap at the territory level in the Iranian EFL context. This study delved into the current L2 self and digital self-authenticity as distinct constructs of L2MSS. Based on the context-specific nature of the psychological factors (Dörnyei & Ryan, 2015 ), especially motivation, further research is strongly recommended to measure university students’ language learning behaviors directly. Further experimental investigations are also needed to replicate our conceptual framework with other proficiency levels and majors to validate it in other EFL and ESL contexts. Further, a qualitative study needs to examine the strengths as well as the weaknesses of the existing conceptual framework. Moreover, it may be interesting to explore the relationship between postgraduate learners’ L2MSS and other technology acceptance models such as diffusion of infusion theory, theory of planned behavior (TPB), or unified theory of acceptance and use of technology (UTAUT).

Data availability

Can be requested from the corresponding author.

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Rahimi, A.R., Mosalli, Z. Exploring the direct and indirect effects of EFL learners’ online motivational self-system on their online language learning acceptance: the new roles of current L2 self and digital self-authenticity. Asian. J. Second. Foreign. Lang. Educ. 9 , 49 (2024). https://doi.org/10.1186/s40862-024-00266-0

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DOI : https://doi.org/10.1186/s40862-024-00266-0

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Design of English online teaching quality evaluation model based on web embedded system and machine learning

• Published: 12 July 2023

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Online education systems have emerged as a new form of education, which has greatly transformed the traditional way of education. This new teaching method provides face-to-face education through live broadcasts over the internet, expanding the boundaries of traditional teaching methods. Moreover, it has improved teaching efficiency and solved the limitation of teaching space, making it an effective supplement to traditional teaching methods. This paper proposes an online English teaching quality evaluation method based on online embedded systems and instrument learning. This method is designed to provide an objective and efficient evaluation of the teaching quality of online English courses. Based on the actual needs of network teaching, this paper has designed a general online English teaching system that includes system management, online education, online examination, resource management, teacher management, and student management functions. This system has been fully tested and proven to meet the needs of network education, which has significantly improved the quality of education. The system is highly scalable and can be easily customized according to the specific requirements of different institutions.

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Research on Online Course Teaching Quality Evaluation Method Based on Internet of Things Technology

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Construction of the Network Online Teaching System Based on the Internet Technology

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This paper was supported by Inner Mongolia Education Department project: College Foreign Language Teaching and Reform based on POA in Local Universities under the background of "Golden Curriculum" – A case study of Chifeng University, NJSY22161.

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Ruan, G. Design of English online teaching quality evaluation model based on web embedded system and machine learning. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08867-6

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DOI : https://doi.org/10.1007/s00500-023-08867-6

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